{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Understanding League of Legends Win Conditions\n",
    "*by Ankush Bharadwaj*\n",
    "\n",
    "### Problem\n",
    "League of Legends is a complicated game involving many different factors that could play into whether or not a team wins a game. To that extent, can I implement a machine learning model that predicts whether or not a game will result in a win or loss, and can this model be used to understand which factors are most crucial for a team to win a game of League of Legends?\n",
    "\n",
    "## Generate DataFrame of Matches\n",
    "First, I will scrape the [100 top summoners](https://www.leagueofgraphs.com/rankings/summoners/na) from the [top 5 most popular regions](https://www.statista.com/statistics/711469/league-of-legends-lol-player-distribution-by-region/). Then, I will connect to the Riot API using the [Python Riot-Watcher](https://github.com/pseudonym117/Riot-Watcher) package, and, through a series of loops, generate a dataframe of roughly 10,000 rows. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Success.\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "print('Success.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Success.\n"
     ]
    }
   ],
   "source": [
    "import re\n",
    "from requests_html import HTMLSession\n",
    "session = HTMLSession()\n",
    "print('Success.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pull_summoner_names(url, start_tag, end_tag):\n",
    "    website_string = session.get(url).html.html\n",
    "    \n",
    "    start_indices = [m.start() for m in re.finditer(start_tag, website_string)]\n",
    "    end_indices = [m.start() for m in re.finditer(end_tag, website_string)]\n",
    "    \n",
    "    if (len(start_indices) != len(end_indices)):\n",
    "        return('List lengths do not match.')\n",
    "    \n",
    "    result = [website_string[(start_indices[i]+len(start_tag)):(end_indices[i])] for i in range(0,100)]\n",
    "    return(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>na</th>\n",
       "      <th>br</th>\n",
       "      <th>kr</th>\n",
       "      <th>euw</th>\n",
       "      <th>eune</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>C9 Zven</td>\n",
       "      <td>xiaolongbao</td>\n",
       "      <td>DWG ShowMaker</td>\n",
       "      <td>Agurin</td>\n",
       "      <td>Litny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Palafox</td>\n",
       "      <td>Aryze</td>\n",
       "      <td>T1 BurdoI</td>\n",
       "      <td>Legendary Manaty</td>\n",
       "      <td>UnExpecteDGanGS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>qpalzmwoiaj</td>\n",
       "      <td>C0ME BACK HOME</td>\n",
       "      <td>mars8</td>\n",
       "      <td>RGE lnspired</td>\n",
       "      <td>Kits&amp;ugrave;ne</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ssumdayday</td>\n",
       "      <td>v f N b</td>\n",
       "      <td>개똥벌레a</td>\n",
       "      <td>Elyoyaaa</td>\n",
       "      <td>Scarlet Rose</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>C9 ZVENNN</td>\n",
       "      <td>VK Hidan</td>\n",
       "      <td>owoowoowoowoowo</td>\n",
       "      <td>SUP Armutke</td>\n",
       "      <td>EXSEMI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>Cazamareas</td>\n",
       "      <td>Scavange</td>\n",
       "      <td>ZED99</td>\n",
       "      <td>Juhozkin</td>\n",
       "      <td>Atmo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>PIux</td>\n",
       "      <td>RNG m1ng</td>\n",
       "      <td>xiaozhabi</td>\n",
       "      <td>RoseLMonster</td>\n",
       "      <td>I Doom Warrior I</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>MentaIIy strong</td>\n",
       "      <td>fvckk</td>\n",
       "      <td>군밤갓</td>\n",
       "      <td>MRS JaVa&amp;aacute;a</td>\n",
       "      <td>Liam Nees&amp;omicron;n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>DrewDozer</td>\n",
       "      <td>esA</td>\n",
       "      <td>Keine1</td>\n",
       "      <td>RGE3</td>\n",
       "      <td>MkdPro123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>FudoMyoo</td>\n",
       "      <td>Spl4sh</td>\n",
       "      <td>HLE Mireu</td>\n",
       "      <td>llamame papi</td>\n",
       "      <td>Zalech23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 na              br               kr                euw  \\\n",
       "0           C9 Zven     xiaolongbao    DWG ShowMaker             Agurin   \n",
       "1           Palafox           Aryze        T1 BurdoI   Legendary Manaty   \n",
       "2       qpalzmwoiaj  C0ME BACK HOME            mars8       RGE lnspired   \n",
       "3        Ssumdayday         v f N b            개똥벌레a           Elyoyaaa   \n",
       "4         C9 ZVENNN        VK Hidan  owoowoowoowoowo        SUP Armutke   \n",
       "..              ...             ...              ...                ...   \n",
       "95       Cazamareas        Scavange            ZED99           Juhozkin   \n",
       "96             PIux        RNG m1ng        xiaozhabi       RoseLMonster   \n",
       "97  MentaIIy strong           fvckk              군밤갓  MRS JaVa&aacute;a   \n",
       "98        DrewDozer             esA           Keine1               RGE3   \n",
       "99         FudoMyoo          Spl4sh        HLE Mireu       llamame papi   \n",
       "\n",
       "                   eune  \n",
       "0                 Litny  \n",
       "1       UnExpecteDGanGS  \n",
       "2        Kits&ugrave;ne  \n",
       "3          Scarlet Rose  \n",
       "4                EXSEMI  \n",
       "..                  ...  \n",
       "95                 Atmo  \n",
       "96     I Doom Warrior I  \n",
       "97  Liam Nees&omicron;n  \n",
       "98            MkdPro123  \n",
       "99             Zalech23  \n",
       "\n",
       "[100 rows x 5 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url = 'https://www.leagueofgraphs.com/rankings/summoners/{}'\n",
    "regions = ['na', 'br', 'kr', 'euw', 'eune']\n",
    "start_tag = '<span class=\"name\">'\n",
    "end_tag = '</span>\\n                                        <br/>\\n'\n",
    "\n",
    "top_summoners = dict()\n",
    "\n",
    "for region in regions:\n",
    "    top_summoners[region] = pull_summoner_names(url.format(region),start_tag,end_tag)\n",
    "\n",
    "summoners = pd.DataFrame(top_summoners)\n",
    "summoners"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Success.\n"
     ]
    }
   ],
   "source": [
    "from riotwatcher import LolWatcher\n",
    "\n",
    "lol_watcher = LolWatcher('app-api-here')\n",
    "print('Success.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>na1</th>\n",
       "      <th>br1</th>\n",
       "      <th>kr</th>\n",
       "      <th>euw1</th>\n",
       "      <th>eun1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>C9 Zven</td>\n",
       "      <td>xiaolongbao</td>\n",
       "      <td>DWG ShowMaker</td>\n",
       "      <td>Agurin</td>\n",
       "      <td>Litny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Palafox</td>\n",
       "      <td>Aryze</td>\n",
       "      <td>T1 BurdoI</td>\n",
       "      <td>Legendary Manaty</td>\n",
       "      <td>UnExpecteDGanGS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>qpalzmwoiaj</td>\n",
       "      <td>C0ME BACK HOME</td>\n",
       "      <td>mars8</td>\n",
       "      <td>RGE lnspired</td>\n",
       "      <td>Kits&amp;ugrave;ne</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ssumdayday</td>\n",
       "      <td>v f N b</td>\n",
       "      <td>개똥벌레a</td>\n",
       "      <td>Elyoyaaa</td>\n",
       "      <td>Scarlet Rose</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>C9 ZVENNN</td>\n",
       "      <td>VK Hidan</td>\n",
       "      <td>owoowoowoowoowo</td>\n",
       "      <td>SUP Armutke</td>\n",
       "      <td>EXSEMI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>Cazamareas</td>\n",
       "      <td>Scavange</td>\n",
       "      <td>ZED99</td>\n",
       "      <td>Juhozkin</td>\n",
       "      <td>Atmo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>PIux</td>\n",
       "      <td>RNG m1ng</td>\n",
       "      <td>xiaozhabi</td>\n",
       "      <td>RoseLMonster</td>\n",
       "      <td>I Doom Warrior I</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>MentaIIy strong</td>\n",
       "      <td>fvckk</td>\n",
       "      <td>군밤갓</td>\n",
       "      <td>MRS JaVa&amp;aacute;a</td>\n",
       "      <td>Liam Nees&amp;omicron;n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>DrewDozer</td>\n",
       "      <td>esA</td>\n",
       "      <td>Keine1</td>\n",
       "      <td>RGE3</td>\n",
       "      <td>MkdPro123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>FudoMyoo</td>\n",
       "      <td>Spl4sh</td>\n",
       "      <td>HLE Mireu</td>\n",
       "      <td>llamame papi</td>\n",
       "      <td>Zalech23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                na1             br1               kr               euw1  \\\n",
       "0           C9 Zven     xiaolongbao    DWG ShowMaker             Agurin   \n",
       "1           Palafox           Aryze        T1 BurdoI   Legendary Manaty   \n",
       "2       qpalzmwoiaj  C0ME BACK HOME            mars8       RGE lnspired   \n",
       "3        Ssumdayday         v f N b            개똥벌레a           Elyoyaaa   \n",
       "4         C9 ZVENNN        VK Hidan  owoowoowoowoowo        SUP Armutke   \n",
       "..              ...             ...              ...                ...   \n",
       "95       Cazamareas        Scavange            ZED99           Juhozkin   \n",
       "96             PIux        RNG m1ng        xiaozhabi       RoseLMonster   \n",
       "97  MentaIIy strong           fvckk              군밤갓  MRS JaVa&aacute;a   \n",
       "98        DrewDozer             esA           Keine1               RGE3   \n",
       "99         FudoMyoo          Spl4sh        HLE Mireu       llamame papi   \n",
       "\n",
       "                   eun1  \n",
       "0                 Litny  \n",
       "1       UnExpecteDGanGS  \n",
       "2        Kits&ugrave;ne  \n",
       "3          Scarlet Rose  \n",
       "4                EXSEMI  \n",
       "..                  ...  \n",
       "95                 Atmo  \n",
       "96     I Doom Warrior I  \n",
       "97  Liam Nees&omicron;n  \n",
       "98            MkdPro123  \n",
       "99             Zalech23  \n",
       "\n",
       "[100 rows x 5 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summoners.columns = ['na1', 'br1', 'kr', 'euw1', 'eun1']\n",
    "summoners"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Success.\n"
     ]
    }
   ],
   "source": [
    "def account_id_for_col(region, column):\n",
    "    temp_list = []\n",
    "    \n",
    "    for summoner in column:\n",
    "        try:\n",
    "            temp_list.append(lol_watcher.summoner.by_name(region, summoner)['accountId'])\n",
    "        except:\n",
    "            print('Error for {}'.format(summoner))\n",
    "            temp_list.append(np.nan)\n",
    "        \n",
    "    return(temp_list)\n",
    "print('Success.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NA1\n",
      "Error for Ne&oslash;&oslash;\n",
      "Error for j1h&ugrave;1V\n",
      "Error for Veni vidi vic&igrave;\n",
      "\n",
      "BR1\n",
      "Error for o&atilde;geN olavaC\n",
      "Error for doutor keio\n",
      "Error for Tr&iuml;go\n",
      "Error for DE BR&Uuml;YNE 17\n",
      "Error for Para&igrave;so\n",
      "Error for Qu&igrave;nn\n",
      "Error for Gord&atilde;o1000Grau\n",
      "\n",
      "KR\n",
      "\n",
      "EUW1\n",
      "Error for Raz&oslash;rk Activoo\n",
      "Error for Rob&iacute;nh00D\n",
      "Error for D&eacute;adly\n",
      "Error for Mj&ouml;llnir Abuser\n",
      "Error for Kang Seung I&omicron;k\n",
      "Error for FNC N&ecirc;mesis\n",
      "Error for zo&iacute;r\n",
      "Error for pr1me&eacute;\n",
      "Error for Legendary Ch&oslash;vy\n",
      "Error for Tigriuk&eacute;\n",
      "Error for MRS X&iacute;co\n",
      "Error for c&oslash;cky movement\n",
      "Error for K&oslash;ldo Locuraa\n",
      "Error for Elyoy&alpha;\n",
      "Error for MRS JaVa&aacute;a\n",
      "\n",
      "EUN1\n",
      "Error for Kits&ugrave;ne\n",
      "Error for Gaarf&igrave;eld\n",
      "Error for H&eacute;tzer\n",
      "Error for L&upsilon;ci&fnof;er\n",
      "Error for &Eta;obbi\n",
      "Error for M&aelig;stro\n",
      "Error for &Mu;&Lambda;TI\n",
      "Error for Humorn&iacute;ček\n",
      "Error for Mart&omicron;\n",
      "Error for Luxur&iacute;a\n",
      "Error for Liam Nees&omicron;n\n"
     ]
    }
   ],
   "source": [
    "print('NA1')\n",
    "summoners['na1_account_id'] = account_id_for_col('na1', summoners.na1)\n",
    "print('\\nBR1')\n",
    "summoners['br1_account_id'] = account_id_for_col('br1', summoners.br1)\n",
    "print('\\nKR')\n",
    "summoners['kr_account_id'] = account_id_for_col('kr', summoners.kr)\n",
    "print('\\nEUW1')\n",
    "summoners['euw1_account_id'] = account_id_for_col('euw1', summoners.euw1)\n",
    "print('\\nEUN1')\n",
    "summoners['eun1_account_id'] = account_id_for_col('eun1', summoners.eun1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>teamId</th>\n",
       "      <th>win</th>\n",
       "      <th>firstBlood</th>\n",
       "      <th>firstTower</th>\n",
       "      <th>firstInhibitor</th>\n",
       "      <th>firstBaron</th>\n",
       "      <th>firstDragon</th>\n",
       "      <th>firstRiftHerald</th>\n",
       "      <th>towerKills</th>\n",
       "      <th>inhibitorKills</th>\n",
       "      <th>baronKills</th>\n",
       "      <th>dragonKills</th>\n",
       "      <th>vilemawKills</th>\n",
       "      <th>riftHeraldKills</th>\n",
       "      <th>dominionVictoryScore</th>\n",
       "      <th>bans</th>\n",
       "      <th>region</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [teamId, win, firstBlood, firstTower, firstInhibitor, firstBaron, firstDragon, firstRiftHerald, towerKills, inhibitorKills, baronKills, dragonKills, vilemawKills, riftHeraldKills, dominionVictoryScore, bans, region]\n",
       "Index: []"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "match_columns = ['teamId', 'win', 'firstBlood', 'firstTower', 'firstInhibitor',\n",
    "       'firstBaron', 'firstDragon', 'firstRiftHerald', 'towerKills',\n",
    "       'inhibitorKills', 'baronKills', 'dragonKills', 'vilemawKills',\n",
    "       'riftHeraldKills', 'dominionVictoryScore', 'bans', 'region']\n",
    "match_df = pd.DataFrame(columns = match_columns)\n",
    "match_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting na1....\n",
      "Skipping index 10, column na1_account_id because nan value.\n",
      "Skipping index 42, column na1_account_id because nan value.\n",
      "Skipping game at index 49, column na1_account_id\n",
      "Skipping index 58, column na1_account_id because nan value.\n",
      "Finished na1\n",
      "Current length of dataframe: 1938\n",
      "\n",
      "Starting br1....\n",
      "Skipping game at index 17, column br1_account_id\n",
      "Skipping game at index 27, column br1_account_id\n",
      "Skipping index 33, column br1_account_id because nan value.\n",
      "Skipping game at index 40, column br1_account_id\n",
      "Skipping game at index 42, column br1_account_id\n",
      "Skipping index 47, column br1_account_id because nan value.\n",
      "Skipping index 52, column br1_account_id because nan value.\n",
      "Skipping game at index 58, column br1_account_id\n",
      "Skipping index 63, column br1_account_id because nan value.\n",
      "Skipping index 68, column br1_account_id because nan value.\n",
      "Skipping index 76, column br1_account_id because nan value.\n",
      "Skipping index 84, column br1_account_id because nan value.\n",
      "Finished br1\n",
      "Current length of dataframe: 3788\n",
      "\n",
      "Starting kr....\n",
      "Skipping game at index 4, column kr_account_id\n",
      "Skipping game at index 67, column kr_account_id\n",
      "Skipping game at index 69, column kr_account_id\n",
      "Finished kr\n",
      "Current length of dataframe: 5782\n",
      "\n",
      "Starting euw1....\n",
      "Skipping index 9, column euw1_account_id because nan value.\n",
      "Skipping game at index 13, column euw1_account_id\n",
      "Skipping index 17, column euw1_account_id because nan value.\n",
      "Skipping game at index 22, column euw1_account_id\n",
      "Skipping index 25, column euw1_account_id because nan value.\n",
      "Skipping index 26, column euw1_account_id because nan value.\n",
      "Skipping index 30, column euw1_account_id because nan value.\n",
      "Skipping index 36, column euw1_account_id because nan value.\n",
      "Skipping index 40, column euw1_account_id because nan value.\n",
      "Skipping game at index 49, column euw1_account_id\n",
      "Skipping index 50, column euw1_account_id because nan value.\n",
      "Skipping index 51, column euw1_account_id because nan value.\n",
      "Skipping game at index 57, column euw1_account_id\n",
      "Skipping games at index 61, column euw1_account_id.\n",
      "Skipping index 67, column euw1_account_id because nan value.\n",
      "Skipping index 69, column euw1_account_id because nan value.\n",
      "Skipping index 75, column euw1_account_id because nan value.\n",
      "Skipping index 92, column euw1_account_id because nan value.\n",
      "Skipping index 93, column euw1_account_id because nan value.\n",
      "Skipping index 97, column euw1_account_id because nan value.\n",
      "Finished euw1\n",
      "Current length of dataframe: 7454\n",
      "\n",
      "Starting eun1....\n",
      "Skipping index 2, column eun1_account_id because nan value.\n",
      "Skipping index 10, column eun1_account_id because nan value.\n",
      "Skipping index 13, column eun1_account_id because nan value.\n",
      "Skipping index 17, column eun1_account_id because nan value.\n",
      "Skipping game at index 36, column eun1_account_id\n",
      "Skipping index 38, column eun1_account_id because nan value.\n",
      "Skipping index 40, column eun1_account_id because nan value.\n",
      "Skipping index 46, column eun1_account_id because nan value.\n",
      "Skipping game at index 55, column eun1_account_id\n",
      "Skipping index 66, column eun1_account_id because nan value.\n",
      "Skipping game at index 69, column eun1_account_id\n",
      "Skipping index 71, column eun1_account_id because nan value.\n",
      "Skipping index 86, column eun1_account_id because nan value.\n",
      "Skipping game at index 90, column eun1_account_id\n",
      "Skipping index 97, column eun1_account_id because nan value.\n",
      "Finished eun1\n",
      "Current length of dataframe: 9226\n",
      "\n"
     ]
    }
   ],
   "source": [
    "account_id_columns = ['na1_account_id', 'br1_account_id', 'kr_account_id', 'euw1_account_id', 'eun1_account_id']\n",
    "\n",
    "for column in account_id_columns:\n",
    "    \n",
    "    current_column = summoners[column]\n",
    "    current_region = column.split('_')[0]\n",
    "    print('Starting {}....'.format(current_region))\n",
    "    \n",
    "    for index in range(0,100):\n",
    "        \n",
    "        if type(summoners[column].iloc[index]) == float:\n",
    "            print('Skipping index {}, column {} because nan value.'.format(index, column))\n",
    "            continue\n",
    "            \n",
    "        try:    \n",
    "            temp_game_ids = [game['gameId'] for game in lol_watcher.match.matchlist_by_account(current_region, summoners[column].iloc[index])['matches']]\n",
    "        except:\n",
    "            print('Skipping games at index {}, column {}.'.format(index, column))\n",
    "            continue\n",
    "        \n",
    "        for gameid in temp_game_ids[:10]:\n",
    "            try:\n",
    "                temp_df = pd.DataFrame(lol_watcher.match.by_id(current_region, gameid)['teams'])\n",
    "                temp_df['region'] = [current_region] * len(temp_df)\n",
    "                match_df = pd.concat([match_df, temp_df], sort = False)\n",
    "            except:\n",
    "                print('Skipping game at index {}, column {}'.format(index, column))\n",
    "                continue\n",
    "    print('Finished {}'.format(current_region))\n",
    "    print('Current length of dataframe: {}\\n'.format(len(match_df)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>teamId</th>\n",
       "      <th>win</th>\n",
       "      <th>firstBlood</th>\n",
       "      <th>firstTower</th>\n",
       "      <th>firstInhibitor</th>\n",
       "      <th>firstBaron</th>\n",
       "      <th>firstDragon</th>\n",
       "      <th>firstRiftHerald</th>\n",
       "      <th>towerKills</th>\n",
       "      <th>inhibitorKills</th>\n",
       "      <th>baronKills</th>\n",
       "      <th>dragonKills</th>\n",
       "      <th>vilemawKills</th>\n",
       "      <th>riftHeraldKills</th>\n",
       "      <th>dominionVictoryScore</th>\n",
       "      <th>bans</th>\n",
       "      <th>region</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100</td>\n",
       "      <td>Fail</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>[{'championId': 245, 'pickTurn': 1}, {'champio...</td>\n",
       "      <td>na1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "    </tr>\n",
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       "      <td>na1</td>\n",
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       "      <td>[{'championId': 777, 'pickTurn': 6}, {'champio...</td>\n",
       "      <td>na1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100</td>\n",
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       "      <td>[{'championId': 777, 'pickTurn': 1}, {'champio...</td>\n",
       "      <td>na1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>200</td>\n",
       "      <td>Fail</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[{'championId': 104, 'pickTurn': 6}, {'champio...</td>\n",
       "      <td>na1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100</td>\n",
       "      <td>Win</td>\n",
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       "      <td>[{'championId': 58, 'pickTurn': 1}, {'champion...</td>\n",
       "      <td>na1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>Fail</td>\n",
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       "      <td>[{'championId': 777, 'pickTurn': 6}, {'champio...</td>\n",
       "      <td>na1</td>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100</td>\n",
       "      <td>Win</td>\n",
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       "      <td>6</td>\n",
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       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[{'championId': 37, 'pickTurn': 1}, {'champion...</td>\n",
       "      <td>na1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>200</td>\n",
       "      <td>Fail</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>[{'championId': 107, 'pickTurn': 6}, {'champio...</td>\n",
       "      <td>na1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  teamId   win firstBlood firstTower firstInhibitor firstBaron firstDragon  \\\n",
       "0    100  Fail      False      False          False      False       False   \n",
       "1    200   Win       True       True          False      False        True   \n",
       "0    100  Fail      False      False          False      False       False   \n",
       "1    200   Win       True       True           True      False        True   \n",
       "0    100   Win       True       True           True      False        True   \n",
       "1    200  Fail      False      False          False      False       False   \n",
       "0    100   Win       True       True          False       True        True   \n",
       "1    200  Fail      False      False          False      False       False   \n",
       "0    100   Win       True      False          False       True       False   \n",
       "1    200  Fail      False       True          False      False        True   \n",
       "\n",
       "  firstRiftHerald towerKills inhibitorKills baronKills dragonKills  \\\n",
       "0           False          0              0          0           0   \n",
       "1           False          1              0          0           2   \n",
       "0           False          0              0          0           0   \n",
       "1            True          8              1          0           2   \n",
       "0           False         10              2          0           1   \n",
       "1           False          0              0          0           0   \n",
       "0            True          4              0          1           2   \n",
       "1           False          0              0          0           1   \n",
       "0           False          6              0          1           2   \n",
       "1            True          4              0          0           3   \n",
       "\n",
       "  vilemawKills riftHeraldKills dominionVictoryScore  \\\n",
       "0            0               0                    0   \n",
       "1            0               0                    0   \n",
       "0            0               0                    0   \n",
       "1            0               1                    0   \n",
       "0            0               0                    0   \n",
       "1            0               0                    0   \n",
       "0            0               1                    0   \n",
       "1            0               0                    0   \n",
       "0            0               0                    0   \n",
       "1            0               1                    0   \n",
       "\n",
       "                                                bans region  \n",
       "0  [{'championId': 245, 'pickTurn': 1}, {'champio...    na1  \n",
       "1  [{'championId': 777, 'pickTurn': 6}, {'champio...    na1  \n",
       "0  [{'championId': 7, 'pickTurn': 1}, {'championI...    na1  \n",
       "1  [{'championId': 777, 'pickTurn': 6}, {'champio...    na1  \n",
       "0  [{'championId': 777, 'pickTurn': 1}, {'champio...    na1  \n",
       "1  [{'championId': 104, 'pickTurn': 6}, {'champio...    na1  \n",
       "0  [{'championId': 58, 'pickTurn': 1}, {'champion...    na1  \n",
       "1  [{'championId': 777, 'pickTurn': 6}, {'champio...    na1  \n",
       "0  [{'championId': 37, 'pickTurn': 1}, {'champion...    na1  \n",
       "1  [{'championId': 107, 'pickTurn': 6}, {'champio...    na1  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "match_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>teamId</th>\n",
       "      <th>win</th>\n",
       "      <th>firstBlood</th>\n",
       "      <th>firstTower</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>[{'championId': 777, 'pickTurn': 6}, {'champio...</td>\n",
       "      <td>na1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[{'championId': 7, 'pickTurn': 1}, {'championI...</td>\n",
       "      <td>na1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>200</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>[{'championId': 777, 'pickTurn': 6}, {'champio...</td>\n",
       "      <td>na1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[{'championId': 777, 'pickTurn': 1}, {'champio...</td>\n",
       "      <td>na1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>200</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[{'championId': 104, 'pickTurn': 6}, {'champio...</td>\n",
       "      <td>na1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>[{'championId': 58, 'pickTurn': 1}, {'champion...</td>\n",
       "      <td>na1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>200</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[{'championId': 777, 'pickTurn': 6}, {'champio...</td>\n",
       "      <td>na1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[{'championId': 37, 'pickTurn': 1}, {'champion...</td>\n",
       "      <td>na1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>200</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>[{'championId': 107, 'pickTurn': 6}, {'champio...</td>\n",
       "      <td>na1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  teamId  win  firstBlood  firstTower  firstInhibitor  firstBaron  \\\n",
       "0    100    0           0           0               0           0   \n",
       "1    200    1           1           1               0           0   \n",
       "0    100    0           0           0               0           0   \n",
       "1    200    1           1           1               1           0   \n",
       "0    100    1           1           1               1           0   \n",
       "1    200    0           0           0               0           0   \n",
       "0    100    1           1           1               0           1   \n",
       "1    200    0           0           0               0           0   \n",
       "0    100    1           1           0               0           1   \n",
       "1    200    0           0           1               0           0   \n",
       "\n",
       "   firstDragon  firstRiftHerald towerKills inhibitorKills baronKills  \\\n",
       "0            0                0          0              0          0   \n",
       "1            1                0          1              0          0   \n",
       "0            0                0          0              0          0   \n",
       "1            1                1          8              1          0   \n",
       "0            1                0         10              2          0   \n",
       "1            0                0          0              0          0   \n",
       "0            1                1          4              0          1   \n",
       "1            0                0          0              0          0   \n",
       "0            0                0          6              0          1   \n",
       "1            1                1          4              0          0   \n",
       "\n",
       "  dragonKills vilemawKills riftHeraldKills dominionVictoryScore  \\\n",
       "0           0            0               0                    0   \n",
       "1           2            0               0                    0   \n",
       "0           0            0               0                    0   \n",
       "1           2            0               1                    0   \n",
       "0           1            0               0                    0   \n",
       "1           0            0               0                    0   \n",
       "0           2            0               1                    0   \n",
       "1           1            0               0                    0   \n",
       "0           2            0               0                    0   \n",
       "1           3            0               1                    0   \n",
       "\n",
       "                                                bans region  \n",
       "0  [{'championId': 245, 'pickTurn': 1}, {'champio...    na1  \n",
       "1  [{'championId': 777, 'pickTurn': 6}, {'champio...    na1  \n",
       "0  [{'championId': 7, 'pickTurn': 1}, {'championI...    na1  \n",
       "1  [{'championId': 777, 'pickTurn': 6}, {'champio...    na1  \n",
       "0  [{'championId': 777, 'pickTurn': 1}, {'champio...    na1  \n",
       "1  [{'championId': 104, 'pickTurn': 6}, {'champio...    na1  \n",
       "0  [{'championId': 58, 'pickTurn': 1}, {'champion...    na1  \n",
       "1  [{'championId': 777, 'pickTurn': 6}, {'champio...    na1  \n",
       "0  [{'championId': 37, 'pickTurn': 1}, {'champion...    na1  \n",
       "1  [{'championId': 107, 'pickTurn': 6}, {'champio...    na1  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "match_df['win'] = match_df['win'].map({'Win': 1, 'Fail': 0})\n",
    "match_df['firstBlood'] = match_df['firstBlood'].astype(int)\n",
    "match_df['firstTower'] = match_df['firstTower'].astype(int)\n",
    "match_df['firstInhibitor'] = match_df['firstInhibitor'].astype(int)\n",
    "match_df['firstBaron'] = match_df['firstBaron'].astype(int)\n",
    "match_df['firstDragon'] = match_df['firstDragon'].astype(int)\n",
    "match_df['firstRiftHerald'] = match_df['firstRiftHerald'].astype(int)\n",
    "match_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>2</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>na1</td>\n",
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       "      <td>na1</td>\n",
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       "      <td>na1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   win  firstBlood  firstTower  firstInhibitor  firstBaron  firstDragon  \\\n",
       "0    0           0           0               0           0            0   \n",
       "1    1           1           1               0           0            1   \n",
       "0    0           0           0               0           0            0   \n",
       "1    1           1           1               1           0            1   \n",
       "0    1           1           1               1           0            1   \n",
       "1    0           0           0               0           0            0   \n",
       "0    1           1           1               0           1            1   \n",
       "1    0           0           0               0           0            0   \n",
       "0    1           1           0               0           1            0   \n",
       "1    0           0           1               0           0            1   \n",
       "\n",
       "   firstRiftHerald towerKills inhibitorKills baronKills dragonKills  \\\n",
       "0                0          0              0          0           0   \n",
       "1                0          1              0          0           2   \n",
       "0                0          0              0          0           0   \n",
       "1                1          8              1          0           2   \n",
       "0                0         10              2          0           1   \n",
       "1                0          0              0          0           0   \n",
       "0                1          4              0          1           2   \n",
       "1                0          0              0          0           1   \n",
       "0                0          6              0          1           2   \n",
       "1                1          4              0          0           3   \n",
       "\n",
       "  riftHeraldKills region  \n",
       "0               0    na1  \n",
       "1               0    na1  \n",
       "0               0    na1  \n",
       "1               1    na1  \n",
       "0               0    na1  \n",
       "1               0    na1  \n",
       "0               1    na1  \n",
       "1               0    na1  \n",
       "0               0    na1  \n",
       "1               1    na1  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "match_df.drop(['vilemawKills', 'dominionVictoryScore', 'bans', 'teamId'], axis = 1, inplace = True)\n",
    "match_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>na1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>na1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>na1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>na1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>na1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>na1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>na1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>na1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  win  firstBlood  firstTower  firstInhibitor  firstBaron  \\\n",
       "0      0    0           0           0               0           0   \n",
       "1      1    1           1           1               0           0   \n",
       "2      0    0           0           0               0           0   \n",
       "3      1    1           1           1               1           0   \n",
       "4      0    1           1           1               1           0   \n",
       "5      1    0           0           0               0           0   \n",
       "6      0    1           1           1               0           1   \n",
       "7      1    0           0           0               0           0   \n",
       "8      0    1           1           0               0           1   \n",
       "9      1    0           0           1               0           0   \n",
       "\n",
       "   firstDragon  firstRiftHerald towerKills inhibitorKills baronKills  \\\n",
       "0            0                0          0              0          0   \n",
       "1            1                0          1              0          0   \n",
       "2            0                0          0              0          0   \n",
       "3            1                1          8              1          0   \n",
       "4            1                0         10              2          0   \n",
       "5            0                0          0              0          0   \n",
       "6            1                1          4              0          1   \n",
       "7            0                0          0              0          0   \n",
       "8            0                0          6              0          1   \n",
       "9            1                1          4              0          0   \n",
       "\n",
       "  dragonKills riftHeraldKills region  \n",
       "0           0               0    na1  \n",
       "1           2               0    na1  \n",
       "2           0               0    na1  \n",
       "3           2               1    na1  \n",
       "4           1               0    na1  \n",
       "5           0               0    na1  \n",
       "6           2               1    na1  \n",
       "7           1               0    na1  \n",
       "8           2               0    na1  \n",
       "9           3               1    na1  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "match_df.reset_index(inplace = True)\n",
    "match_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>win</th>\n",
       "      <th>firstBlood</th>\n",
       "      <th>firstTower</th>\n",
       "      <th>firstInhibitor</th>\n",
       "      <th>firstBaron</th>\n",
       "      <th>firstDragon</th>\n",
       "      <th>firstRiftHerald</th>\n",
       "      <th>towerKills</th>\n",
       "      <th>inhibitorKills</th>\n",
       "      <th>baronKills</th>\n",
       "      <th>dragonKills</th>\n",
       "      <th>riftHeraldKills</th>\n",
       "      <th>region</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>na1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>na1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>na1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>na1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>na1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>na1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>na1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>na1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>na1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>na1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   win  firstBlood  firstTower  firstInhibitor  firstBaron  firstDragon  \\\n",
       "0    0           0           0               0           0            0   \n",
       "1    1           1           1               0           0            1   \n",
       "2    0           0           0               0           0            0   \n",
       "3    1           1           1               1           0            1   \n",
       "4    1           1           1               1           0            1   \n",
       "5    0           0           0               0           0            0   \n",
       "6    1           1           1               0           1            1   \n",
       "7    0           0           0               0           0            0   \n",
       "8    1           1           0               0           1            0   \n",
       "9    0           0           1               0           0            1   \n",
       "\n",
       "   firstRiftHerald towerKills inhibitorKills baronKills dragonKills  \\\n",
       "0                0          0              0          0           0   \n",
       "1                0          1              0          0           2   \n",
       "2                0          0              0          0           0   \n",
       "3                1          8              1          0           2   \n",
       "4                0         10              2          0           1   \n",
       "5                0          0              0          0           0   \n",
       "6                1          4              0          1           2   \n",
       "7                0          0              0          0           1   \n",
       "8                0          6              0          1           2   \n",
       "9                1          4              0          0           3   \n",
       "\n",
       "  riftHeraldKills region  \n",
       "0               0    na1  \n",
       "1               0    na1  \n",
       "2               0    na1  \n",
       "3               1    na1  \n",
       "4               0    na1  \n",
       "5               0    na1  \n",
       "6               1    na1  \n",
       "7               0    na1  \n",
       "8               0    na1  \n",
       "9               1    na1  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "match_df.drop(['index'], axis = 1, inplace = True)\n",
    "match_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb306b84110>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('darkgrid')\n",
    "plt.figure(figsize = (12,8))\n",
    "cmap = sns.diverging_palette(220, 20, l = 40, s = 99, sep = 20, center = 'light', as_cmap = True) \n",
    "\n",
    "sns.heatmap(match_df.isna(), cmap = cmap, yticklabels = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>win</th>\n",
       "      <th>firstBlood</th>\n",
       "      <th>firstTower</th>\n",
       "      <th>firstInhibitor</th>\n",
       "      <th>firstBaron</th>\n",
       "      <th>firstDragon</th>\n",
       "      <th>firstRiftHerald</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>9226.000000</td>\n",
       "      <td>9226.000000</td>\n",
       "      <td>9226.000000</td>\n",
       "      <td>9226.000000</td>\n",
       "      <td>9226.00000</td>\n",
       "      <td>9226.000000</td>\n",
       "      <td>9226.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.499566</td>\n",
       "      <td>0.496206</td>\n",
       "      <td>0.388142</td>\n",
       "      <td>0.27726</td>\n",
       "      <td>0.439627</td>\n",
       "      <td>0.439302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.500027</td>\n",
       "      <td>0.500027</td>\n",
       "      <td>0.500013</td>\n",
       "      <td>0.487354</td>\n",
       "      <td>0.44767</td>\n",
       "      <td>0.496369</td>\n",
       "      <td>0.496329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               win   firstBlood   firstTower  firstInhibitor  firstBaron  \\\n",
       "count  9226.000000  9226.000000  9226.000000     9226.000000  9226.00000   \n",
       "mean      0.500000     0.499566     0.496206        0.388142     0.27726   \n",
       "std       0.500027     0.500027     0.500013        0.487354     0.44767   \n",
       "min       0.000000     0.000000     0.000000        0.000000     0.00000   \n",
       "25%       0.000000     0.000000     0.000000        0.000000     0.00000   \n",
       "50%       0.500000     0.000000     0.000000        0.000000     0.00000   \n",
       "75%       1.000000     1.000000     1.000000        1.000000     1.00000   \n",
       "max       1.000000     1.000000     1.000000        1.000000     1.00000   \n",
       "\n",
       "       firstDragon  firstRiftHerald  \n",
       "count  9226.000000      9226.000000  \n",
       "mean      0.439627         0.439302  \n",
       "std       0.496369         0.496329  \n",
       "min       0.000000         0.000000  \n",
       "25%       0.000000         0.000000  \n",
       "50%       0.000000         0.000000  \n",
       "75%       1.000000         1.000000  \n",
       "max       1.000000         1.000000  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "match_df.to_csv('matches.csv')\n",
    "match_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 9226 entries, 0 to 9225\n",
      "Data columns (total 13 columns):\n",
      " #   Column           Non-Null Count  Dtype \n",
      "---  ------           --------------  ----- \n",
      " 0   win              9226 non-null   int64 \n",
      " 1   firstBlood       9226 non-null   int64 \n",
      " 2   firstTower       9226 non-null   int64 \n",
      " 3   firstInhibitor   9226 non-null   int64 \n",
      " 4   firstBaron       9226 non-null   int64 \n",
      " 5   firstDragon      9226 non-null   int64 \n",
      " 6   firstRiftHerald  9226 non-null   int64 \n",
      " 7   towerKills       9226 non-null   object\n",
      " 8   inhibitorKills   9226 non-null   object\n",
      " 9   baronKills       9226 non-null   object\n",
      " 10  dragonKills      9226 non-null   object\n",
      " 11  riftHeraldKills  9226 non-null   object\n",
      " 12  region           9226 non-null   object\n",
      "dtypes: int64(7), object(6)\n",
      "memory usage: 937.1+ KB\n"
     ]
    }
   ],
   "source": [
    "match_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 9226 entries, 0 to 9225\n",
      "Data columns (total 13 columns):\n",
      " #   Column           Non-Null Count  Dtype \n",
      "---  ------           --------------  ----- \n",
      " 0   win              9226 non-null   int64 \n",
      " 1   firstBlood       9226 non-null   int64 \n",
      " 2   firstTower       9226 non-null   int64 \n",
      " 3   firstInhibitor   9226 non-null   int64 \n",
      " 4   firstBaron       9226 non-null   int64 \n",
      " 5   firstDragon      9226 non-null   int64 \n",
      " 6   firstRiftHerald  9226 non-null   int64 \n",
      " 7   towerKills       9226 non-null   int64 \n",
      " 8   inhibitorKills   9226 non-null   int64 \n",
      " 9   baronKills       9226 non-null   int64 \n",
      " 10  dragonKills      9226 non-null   int64 \n",
      " 11  riftHeraldKills  9226 non-null   int64 \n",
      " 12  region           9226 non-null   object\n",
      "dtypes: int64(12), object(1)\n",
      "memory usage: 937.1+ KB\n"
     ]
    }
   ],
   "source": [
    "match_df[['towerKills', 'inhibitorKills', 'baronKills', 'dragonKills', 'riftHeraldKills']] = match_df[['towerKills', 'inhibitorKills', 'baronKills', 'dragonKills', 'riftHeraldKills']].astype(int)\n",
    "match_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>win</th>\n",
       "      <th>firstBlood</th>\n",
       "      <th>firstTower</th>\n",
       "      <th>firstInhibitor</th>\n",
       "      <th>firstBaron</th>\n",
       "      <th>firstDragon</th>\n",
       "      <th>firstRiftHerald</th>\n",
       "      <th>towerKills</th>\n",
       "      <th>inhibitorKills</th>\n",
       "      <th>baronKills</th>\n",
       "      <th>dragonKills</th>\n",
       "      <th>riftHeraldKills</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>9226.000000</td>\n",
       "      <td>9226.000000</td>\n",
       "      <td>9226.000000</td>\n",
       "      <td>9226.000000</td>\n",
       "      <td>9226.00000</td>\n",
       "      <td>9226.000000</td>\n",
       "      <td>9226.000000</td>\n",
       "      <td>9226.000000</td>\n",
       "      <td>9226.000000</td>\n",
       "      <td>9226.000000</td>\n",
       "      <td>9226.000000</td>\n",
       "      <td>9226.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.499566</td>\n",
       "      <td>0.496206</td>\n",
       "      <td>0.388142</td>\n",
       "      <td>0.27726</td>\n",
       "      <td>0.439627</td>\n",
       "      <td>0.439302</td>\n",
       "      <td>4.611207</td>\n",
       "      <td>0.684587</td>\n",
       "      <td>0.363971</td>\n",
       "      <td>1.554195</td>\n",
       "      <td>0.700087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.500027</td>\n",
       "      <td>0.500027</td>\n",
       "      <td>0.500013</td>\n",
       "      <td>0.487354</td>\n",
       "      <td>0.44767</td>\n",
       "      <td>0.496369</td>\n",
       "      <td>0.496329</td>\n",
       "      <td>3.413101</td>\n",
       "      <td>0.957761</td>\n",
       "      <td>0.583586</td>\n",
       "      <td>1.340606</td>\n",
       "      <td>0.738059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               win   firstBlood   firstTower  firstInhibitor  firstBaron  \\\n",
       "count  9226.000000  9226.000000  9226.000000     9226.000000  9226.00000   \n",
       "mean      0.500000     0.499566     0.496206        0.388142     0.27726   \n",
       "std       0.500027     0.500027     0.500013        0.487354     0.44767   \n",
       "min       0.000000     0.000000     0.000000        0.000000     0.00000   \n",
       "25%       0.000000     0.000000     0.000000        0.000000     0.00000   \n",
       "50%       0.500000     0.000000     0.000000        0.000000     0.00000   \n",
       "75%       1.000000     1.000000     1.000000        1.000000     1.00000   \n",
       "max       1.000000     1.000000     1.000000        1.000000     1.00000   \n",
       "\n",
       "       firstDragon  firstRiftHerald   towerKills  inhibitorKills   baronKills  \\\n",
       "count  9226.000000      9226.000000  9226.000000     9226.000000  9226.000000   \n",
       "mean      0.439627         0.439302     4.611207        0.684587     0.363971   \n",
       "std       0.496369         0.496329     3.413101        0.957761     0.583586   \n",
       "min       0.000000         0.000000     0.000000        0.000000     0.000000   \n",
       "25%       0.000000         0.000000     2.000000        0.000000     0.000000   \n",
       "50%       0.000000         0.000000     4.000000        0.000000     0.000000   \n",
       "75%       1.000000         1.000000     8.000000        1.000000     1.000000   \n",
       "max       1.000000         1.000000    11.000000        7.000000     3.000000   \n",
       "\n",
       "       dragonKills  riftHeraldKills  \n",
       "count  9226.000000      9226.000000  \n",
       "mean      1.554195         0.700087  \n",
       "std       1.340606         0.738059  \n",
       "min       0.000000         0.000000  \n",
       "25%       0.000000         0.000000  \n",
       "50%       1.000000         1.000000  \n",
       "75%       3.000000         1.000000  \n",
       "max       5.000000         2.000000  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "match_df.to_csv('matches.csv')\n",
    "match_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis\n",
    "\n",
    "Now, I will perform my Exploratory Data Analysis. First, I will see the proportion of won games where the winning team had First Blood, First Tower, First Inhibitor, First Baron, and First Dragon. Next, I will visualize the correlation between all the columns, first overall, then for each region to see if there's any variation between regions. Finally, I will perform a Principal Component Analysis on the data to see if there are any obvious patterns in the relevance of each column to the variance in my data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "firstBlood        0.588848\n",
       "firstTower        0.702490\n",
       "firstInhibitor    0.913432\n",
       "firstBaron        0.802189\n",
       "firstDragon       0.629684\n",
       "dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(match_df.groupby(['win']).sum().iloc[1]/(match_df.groupby(['win']).sum().iloc[1]+match_df.groupby(['win']).sum().iloc[0]))[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb305de5410>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (12,8))\n",
    "\n",
    "cmap = sns.diverging_palette(220, 20, l = 40, s = 99, sep = 20, center = 'light', as_cmap = True) \n",
    "sns.heatmap(match_df.drop(['region'], axis = 1).corr(), vmin = 0, vmax = 1, annot = True, cmap = cmap, lw = .5, linecolor = 'white')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb306115350>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (12,8))\n",
    "sns.heatmap(match_df[match_df.region == 'na1'].drop(['region'], axis = 1).corr(), vmin = 0, vmax = 1, annot = True, cmap = cmap, lw = .5, linecolor = 'white')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb307b95ad0>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (12,8))\n",
    "sns.heatmap(match_df[match_df.region == 'br1'].drop(['region'], axis = 1).corr(), vmin = 0, vmax = 1, annot = True, cmap = cmap, lw = .5, linecolor = 'white')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb30882c2d0>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (12,8))\n",
    "sns.heatmap(match_df[match_df.region == 'kr'].drop(['region'], axis = 1).corr(), vmin = 0, vmax = 1, annot = True, cmap = cmap, lw = .5, linecolor = 'white')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb308b99c10>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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czopOTCXQu+wl87yCIrTFejyrVL7b6hZTt1FjjuzdDcC5UyfxD6plKhvy+lt88v1CPpr3LZ16PcZjzwyiaas2JCecZ9aEd3nrwyk0bd3WonlPXUqnZUDJDTYh3u4kZGXfc52BoQ3oWrcmAIW6YgxGwz3WuL/OXM6guX9J3avj6UbilXvXvWeaN6DPjboX4FaFjFzL1r16/j4cjEkE4EzSZQK83MqU5xUWoSvW42nh6Un/pEm9YHYdPArAibPnqB1wa/Q48WIqL4ydhF5vQFdczNHTMdQNCmTf0RO8Mugpvv54PCpFoVUTy51o1mrQkJP79wEQH32KajVrmsouJSXx1aTxpvZNY2ODSqVw7sQx2nR/lHc+n4O7jy+1GljnxLhOg0Ycv3GSG3f6FNVr3ppTPfB/b/DRNz/y/pfzaf9ob3qGD6SxlaYkJFy5RsiNttfftfIdN0y/2LIxqTnXWXX8LDe7u51rB9C8eslNh1q93ioDI/eTolKb5cfSHsqbx+rUqUNKSgoZGRm8/fbbfPPNN2zbto2PP/6YiIgI03LW6MT8F+w9n0zT6t7MeKIbCgpfRO6jX+O6pF67bhoZut3Ko6cZ07UNoTV8MRiNfBG5z6KZ67dqy7ljR5g3diRGYMCbo4la+xtu3r7Ub9kan8CazBs7EhSF4GahBDVohE9AIMtmTefMoQOo1WoGvPW2RTPf1LZ+bQ6fS+SteUswGmHMgB6sijqEr1sV2tSvRUpmNt5VrdupBWjR8RFOHNzPhJdfxGg08vqED1i3dDHeftUJa9/xrussmf8VOq2Wn774HABHJ+c7bjgzl11xSTT39+XLp3uiKDB9y26ealqPi1evszfh7vOpN0XHMbZbO3rWr41aUZi+ZbdFst60LyGFJn7efNavK6Dw5fZ9PN4omMvXcjmQePe699vRaEZ3aU2ovy96g4E5f1m27rWtF8SRuGRGfrMKjEZGP9mV33YdxdetCq1DAknJvIqX64PTqQXo3DqMfUdP8NzbEwEjH438H4vW/El1H286tQqlZ6d2DHl7Ahq1hj5dOlCrRnUKCgr58IsF2NhoCPKvznuvWe4JKs3adSD68EGmjngVMPLi2PFsXrkMT18/mrZtR/WgWkwd8QqKotCwRSuCGzcl7WIK338yBQBXd3deeOc9i+UtLbRDJ04dOshH/3sJI0ZeHvc+G5YvwauaH83bdbBKprs5lZpOHY+qvNE+FBRYfiSaDkH+ZOXloygKNd2roFarqHtjrv6G03EcSEplYLN6tKhRDRWw7Ei0dXdCAKAY/+unGH9jxowZpKSkMGfOHGbOnElcXBwTJkxg9OjRrFixgs6dO7Nx40bs7Oz4/PPPqVmzJk88ce/HjCxv/d85FwjfWzJf6LGvl1g5Sfn9+dog1p69YO0Y5da3bgAASWu/tW6Qf8G/b8l8thNZ1plX/n/RyK0SXeYstHaMctv21lAA+i5YauUk5bf21YEAXFg118pJyi/gqTcoiDtm7Rjl5lCr5Oaz3RczrJyk/NpW8+Bg2r2vdjxIwrxcefv3rdaOUW4z+3W1dgQA/nis2r0X+j94/M+7n5Sby3+nl/YvvfPOrUe3vP32rZG4FStWABAZGWl6b8yYMZYLJoQQQgjxgJG/PCaEEEIIIcQD5KEdsRVCCCGEEOVjjWfOmsPDsRdCCCGEEKLCkxFbIYQQQoiK7iGZYysdWyGEEEKICk6mIgghhBBCCPEAkRFbIYQQQogKTkZshRBCCCGEeIDIiK0QQgghRAUnf6BBCCGEEEKIB4iM2AohhBBCVHQPyRxb6dgKIYQQQlRwiqJYO8J9oRiNRqO1QwghhBBCCOvZ9Ew9s2z30WXRZtnu35ERWyGEEEKICu5huXlMOrb/0mNfL7F2hHL787VBACxv/d/5NYfvLeaR2T9ZO0a5/TXqBQCaTp1v5STld3TC/wCoM+kLKycpv3OTR/LEt8utHaPcVr8cDkD7mT9YOUn57Xx7GAC95v1q5STlt+H1wYR98o21Y5TbwfdeAcB/3AwrJym/pE/focuchdaO8a9se2sozy5cY+0Y5bZ4aH9rR3io/Hd6PEIIIYQQwiwUtdraEe4L6dgKIYQQQlR0D8lUhIdjL4QQQgghRIUnI7ZCCCGEEBXcw/K4LxmxFUIIIYQQDwUZsRVCCCGEqOAU+ctjQgghhBDioSA3jwkhhBBCCPHgkBFbIYQQQogKTlE9HM+xlRFbIYQQQgjxUJARWyGEEEKICk5RyeO+hBBCCCGEeGDIiK0QQgghRAWnPCRPRZCOrZkpwGsdwwh0c0Wn1/PlX/u5lJNbZpnK9nZ8/kR3Xl++Hp3eAMDC5/qReu06AGfTMlm477ilo/+jqvVa0Pj1T/jr9S7WjgKUfM4ju7QmyL0qOr2eGVt2mz6/m1wc7Pgq/DFeXPQ7Or0eJ1sbxvfsgJOtLRqViq+jDhB9KcPsOcf37EAdTze0ej2T128nOTvHVN6/SQhPNatHscHI97sOszMuEV+XSnz8eGdA4dK160zZsIPC4mIAXB3t+Xlof57+dgVavf7+51Xgw8c6U9fbA22xnglrt5B05ZqpfEDzBoSHNkRvMPD1jgNsP5eAg42Gj/p0xs/VBRu1mo/X/8WJi2n0aRTMi22aYzAaWHUkmqUHT9z3vGWyAy+3a06AWxV0egNfRx3k8l3q3rS+XRi1ahM6vQGVovB8qybU8qiKRq1i+eFTHE66ZNact2ce3bUNtTzc0On1fBaxk4tXyx7HVRzs+XrgYzy/cE2Z33n7WjV4pE4gkzdst1jem5lf79iCQPeSz3nOX/u4dO3Oz3nmk915bdmtNg7Ar0plZj/Vg0E//VbmfXPlfLdHe2p7uaEr1jNl4w5SStW9fo3r0r9pPfQGAz/uOcKuuCR8XSrxwWOPoChw+VouUzdGUVRczLMtGtG9Xi2MGPlpz1G2n7tw//MqMLVvN0J8PNHqixn722YSs66aygeGNWJwy8YUGwzMjdzLtrPncXGwZ8eYYcSkZQKw+XQsP+4+Ytrez88/yZboOBbvN//3iQK81bkVQe6uaPUGZm7dc9c2+csBvRi+eC06vQF7jYbxj7ansr0dBbpiPo3YybWCIrNnvZn3+VZN8Hd1odig5/s9R0m7nldmmUp2tnzQqyPvrd2GzmDAwUbDiA5h2NloKNYbmL/zENcKLZPXLOQ5tv+OXq/n5ZdfJjY2llGjRtG/f/97rhMTE0NOTg5hYWEMGTKEgoICHBwcKCgooG3btowaNYr9+/ezbNkyZs+e/X/OtnTpUjIzM3njjTf+z9v4O61q+mGjVjNmdQTBXm4Ma9uMKRujTOXNqvswtFVjqjjam97zqexMfGY2kzfsuO957oe6g8dQo+dg9AX51o5i0q5WDWzVakYsX0+ItwevdWzB+39sM5WH1fDlpXahuJb6nJ9uXp8jSZf47Wg01V0r837PTryy5A+z5nwkOBBbtZqhC9fQ0NeL0V3bMGrlJgDcnBwYGNaQwT+uwk6j4cfn+rEvIZlRXVqz8kg0m07H0r9JCM+2bMT3u4/QumZ13nykJVWdHM2Wt1vdIOw0GsK/W05jP2/G9ejAa0vXAeDu7MiQVk14YsFS7DRqlg4fwO74JIa3C+VcehZjV0cQ7OVOXW93TlxM490eHej91S/ka3VsGPEc60/GkGPGL4EWAdWwUat5b+026ni68XyrJnwasctU3sTPm2dbNKKKw61jomPtGmhUKsb/sY2qjg60qVkdsFzHtn2tGtip1fxv6Trq+XjweseWjF+79dY+1ajGKx3CqOroUGa9Nx9pRYuAasSlX7FY1pta16yOjVrF27+VtHHD2zbj4w1l27gXWjfB9bbMDjYahrdtZvYO7U2d6gRip1Ez7JffaeDrycjOrRnz22agpO6FhzbguZ9XY6vR8P2zj7M/IYU3O7di9dFoNkfH0bdxXQa3aMiKw6cJD21A/wXLcLDV8OuLT5mlY9ujXm3sbDT0n/8rTav7MLF3J4b/8jsAHs5OvNC2GY/NXYSdRs1v/xvEzthEGlbzYu3xs3xQqu276Z3u7csc6+bWNsgfW7WaN1ZsJMTbnVfbhzLpz79M5aH+vgxv2wzXUpl6NahNbHoWiw6coEdIEM+GNWJe1EGL5G3u74uNWsVHG3cQ5O7KoNCGzP5rn6m8oa8n4c3q42JvZ3qvQ60aJF/NYdnh03SqHUDvBrVZcuiURfKKv2ex7nlGRgbZ2dlERUWVq1MLEBERQVxcnOn1Z599xqJFi1i5ciUHDhzg5MmT5op739T39uTIjRGfmLQsantULVNuMBp5/49Irpf6gq/lWZWqTg5M69uFD3t3olqVShbNfC+5F+PZPe5pa8coo6GvJwcuXATgzOUM6ni5lSk3GGHMb5vLfM4rj0Sz7kQMAGqVyiwjnrdrWt2HPeeTATiZmkY9Hw9TWQNfL46nXEanN5BbpCU5+xq1Pd2o6e7K7vhEAI4lX6JJdZ8b+2Tk1SXryCkoNFve5jWqsTP2AgDHUy7TsJqXqaxRNW+OJKWi0+vJLdKSlHWVul7utKtVA53ewA/P9ee1ji3YGVeSPSYtk0p2dthqNCgKGM2WukSItwdHU0rq3rn0LII8XMuUG41GPly/ndwirem9Jn7eZOXlM+HR9vyvQygHEy+aOWVZjap5s//GcRx9KYO6Xu5lyg0YGbVy4x0nBKdS05i5dY/FcpZW38fDNKpd0saVrXtGo5Hxa7eVqXsAbz7SkoX7jlF04+qDuTWu7m2qe6dS0wkpVffq+3hyPCUNnd5AXpGW5Owcanu6Eejuyp7zSUDJ8d+kug8FumIu5eTiYKvBwcYGg9E8R3JYgB/bYxIAOJp8iUbVvE1lTap7c+jCRbR6PdeLtFzIukpdHw8aVvOioa8XK15+hvmDHsezkhMAvRrUwWA0sv1cglmy3k1DX09T/TlzOZPg245lI0bGrtnC9VL1b/WxM/x6sOR73bOSE9lmbNtuF+zpxomLaQDEZ2YT6F6lbF6jkU+37CZXeytvcvY17G1KxgcdbDToDeZu1cxLUVRm+bE0i43YTpw4kQsXLjBp0iRCQkKoWbMmn3/+OTY2NgwYMICEhAT27duHwWCgd+/e9OzZkzVr1mBjY0P9+vXLbEur1VJcXIynpycXLlwwvf/HH3+wcOFCbG1tCQgIYPLkyQCMHz+e5ORk9Ho9L7zwAr169eLQoUNMmzYNFxcXVCoVTZo0Mct+O9hqyCtVEfRGIypFMTWGx1Iu37HOlbwCVh45ze74ZOp5e/B21zaMXrXZLPn+L1K2r8HRu4a1Y5ThaGtb5nM2GMp+zoeTUu9YJ+9Gg+rq6MD4Rzswb/t+s+d0srMt05HSG4yoFQW90YiTnQ3XC2+V5RfpqGRnS0xaFp1qB7LuZAwd6wTgYGMDwP6EFLPndbazLfPFozcYUKsU9AZjSVmpvHlaLZXsbXF1tMfF3o5hv6yhX+MQxvVoz9jVEcSmZbL61UEU6HRERMfd0dG53xxtbcjX6kyvDbfVveM3vsRKq2xvh49LJaZu2kk9Hw9GdGrBxHV/3bGcuTjZ2ZQ5PgzGW8cHwKHEO49jgMiYBJr4ed+1zNzu9TkfvUsbNzisIQcvpJJQ6tK6uTnZ2pjqPIDBYChV98rWy3ytDmc7W86lZdKhdgDrT56jQ+0apk5MWk4uK14agEpR8fPeo2bJ62xvW6aO6I3GUnXPrkxZXpGWyvZ2xGVc4eTF3eyKS6RfkxAmP96FWVv30LdJCK/+upaRXdqYJevdONrakFd067jQGw23tcl3vxJiMBr5/InuBLq5MnZNhEWyQknHNF936yTr9u+QU3eZppZbpKWhryef9e2Ck50tH5e6Giusx2Id2w8++IDRo0fj4XHrLLmoqIiVK1cC0LFjRxYvXoyXlxerV6/Gy8uL/v374+7uTqNGjQB49913cXBwIDk5mbp16+Lq6mrq2GZnZzN37lzWrFmDs7Mz06ZNY/ny5QC4uroyY8YMcnNzeeKJJ2jVqhWffPIJM2fOJDAwkA8++MBs+12gLTZ1RIAyFeXvxGVcMZ35RV/OwM3J4R+XF5Cv1eJo++8+Z4BAN1cm9e7I/KiDd+3o3G95RXfmvNlpySvS4WR3q7usq1wAACAASURBVMzRzobrRVpmbdvDuB7t6VG/FgcuXORqQYHZc96UW6TF6fa8N47N3CJtmbxOtrbkFBZxNb+QbWfPAxAZc56X24cS7OVOpzqBdJ79I/laHZ8/+SiP1q/NptOxZsuer9WVrXvc+5i4XqTl0I2ToOhLGfi6WPZqSV6RrszxoZQ6Ph5U+VodDra3vkrKU/ceCQ4kMzef7vWCcHV0YOrjXRi7ZotZc+Zp//6zzbvtWHa0teF6YRFfbNvH2O5t6VHvRt3LL6Rtzeq4OzvS9+ulAMx9phfHUy7f9/n5uYVanO1sTa/L1r0inEqVOdnZklNQyNGkVApudM42nY7l7W7teKpZfbwrO7PspXD8XF3QFetJzr7GDjNMnyjtjuOiHPXvpjGrI6juWplpj3dlyMLV5opYRoGuGAfNvzuO+zcOYf2pWCLPXaC6a2Xe6tSS8esizR3VbJSHZI6tVfciMDDQ9O9Zs2Yxa9Yshg0bRk5Ozl2XvzkVITIykipVqvD999+bypKTk6lVqxbOzs4AhIWFERsbS3x8PGFhYQA4OzsTFBREcnIyaWlppv9/s2bNzLWLRF/OILSGLwDBXm5cKMcIxcDQhvRtFAxAoFsVMq4/OHNZH1SnUtNpGeAHlFyCPp+Zfc91alR14cPHOjFlww7TNAZzO5Z8mXZB/gA09PUiLuPWnMhTqWk0re6DrVqNs50tgW6uxKVfoVWgH9/sPMiIZesxGo3ss8BI7U2Hk1LpWKeknjT28+Zcepap7MTFy4TWqIatpiRvkEdVzqVn3VgnAICwgGrEpmdxvbCIwuJiioqLMRiNZOXll5mrZg5n0zJpdmPaRh1PNxJL3fT2d85czjCtE1C1Cpm5lq17J1PTaB1YchzX8/HgfKbl58z+W9GXMgj1/3dt3PDFfzDu962M+30r2fkFTLjLnND77XjKZdreqHsNfD2JL1X3Tl9Kp4mfN7ZqNU52tgS6VyE+I5uWgdX4btdh3ly+AaPRyIELKeQUFlFUrEerL/m5XqilkhmO5UOJF3mkbk2gZArT2cu3Os7Hki/TItAPO42aSna21PKoSkxaJtOffJReDeoAJfcdnLx4mWkbd9D3618J/3Y5qw6f4vtdh8zeqQU4dal0m+xOQta92+SBoQ3oemOfC3XFGIyWmX8NJdOVGvuVTLUKcnclOfve7UWeVmu6WpFTUFSmI/9fpKhUZvmxNKv+FlQ3dlir1bJp0yZmzZqF0Wikd+/e9O7dG0VRMBjuPLBVKhVeXl7odLcuc/j5+REfH09+fj6Ojo4cOHCAwMBANBoNhw4dolu3buTm5nLu3Dn8/Pzw8PAgPj6eoKAgTp48iYuLi1n2ce/5ZJpW92bGE91QUPgich/9Gtcl9dr1v+1MrTx6mjFd2xBawxeD0cgXkfvuupy4ZWdcIs1r+DI3vDcK8FnELp5uVp+LV3NM8+pu91K70JIbzjq1BEpGdN438xdsZMx5WtX04+eh/VGAD/78i2dbNCI5O4cdsRdYevAkPz7XD0VRmLf9AFq9ngtZV/nwsUfQFuuJz8zm0007zZqxtC1n4mgb5M+y4QNQFIX31kTwQpumJGZdIzLmPIv2HWPpsKdRFIVZ2/agLdazIOoAU/t2Y/lL4RTr9YxdHUHqtessO3iSpcMGoNPrSbpyjdXHos2afX9CCo2reTHt8S4oCny1/QB9Gtbhck4uB//mkv6WM+d5pX1zPu3bFRT4Zudhs2a8XVTsBUJr+PL1wMdQUPhkcxThzRuQcjWH3fFJFs1SXnvOJ9O0ug+fP9EdRYHZ2/bR/0Ybt99CJ4zlsT0mgZYBfvwwpC8oCpP/3M6gsIakZOcQFZfI8kOn+G7I4ygofL3jIFq9nsQr15jYuxM6vZ7zGdl8FrELvcFA9KUMfhraD4MRjidfNsu0oE2nz9G+Vg1W/28QCgpjVm1keLtQErOy2XImnp92H2HVKwNRKQozInZRVKzn001RzHjqUYa0bkK+Vse7v1lvCtuuuCSa+/vy5dM9URSYvmU3TzWtx8Wr19mbcPc2eVN0HGO7taNn/dqoFYXpW3ZbLO+hpFQa+HoyqWcHFBS+3X2YnvVqkXY9lyPJd06nAVh19AzD2zSla92aqBUVP+wxz7QU8e8oRqNlrnOlpKQwevRo2rdvj7u7OzVr1izzNIOvvvqKiIgIXFxcCA4OZsKECezYsYPp06czadIk5s2bZ3oqAoC9vT0zZswgJibGtJ1169axcOFCVCoV/v7+TJ06FUVRmDhxIklJSRQVFTFkyBD69+9PXFwc48aNw8nJCScnJ0JCQsr1VITHvl5i1s/pfvrztUEALG/93zmLDN9bzCOzf7J2jHL7a9QLADSdOt/KScrv6IT/AVBn0hdWTlJ+5yaP5Ilvl1s7RrmtfjkcgPYzf7BykvLb+fYwAHrN+9XKScpvw+uDCfvkG2vHKLeD770CgP+4GVZOUn5Jn75DlzkLrR3jX9n21lCeXbjG2jHKbfHQ8t1Qb25Rb3Y3y3Y7fGm5udJgwRFbPz8/VqxYUea9li1bmv49YsQIRowYUaa8U6dOdOrUCYBWrVrddbstW7Y0badPnz706dPnjmU+++yzO96rVasWq1at+lf7IIQQQgghHlz/naE8IYQQQghhFvKXx4QQQgghxENBnooghBBCCCHEA0RGbIUQQgghKjpFsXaC+0JGbIUQQgghxENBRmyFEEIIISo4RaW2doT7QkZshRBCCCHEQ0FGbIUQQgghKriH5akI0rEVQgghhKjgHpbn2D4ceyGEEEIIISo8GbEVQgghhKjgFJU87ksIIYQQQogHhmI0Go3WDiGEEEIIIaxn3/inzbLdVtNWmmW7f0emIvxLa89esHaEcutbNwCAR2b/ZN0g/8Jfo15geev/zmEZvrcYgNlRh6ycpPxGdQgFoMmUr62cpPyOvf8adSZ9Ye0Y5XZu8kgAkgqLrZyk/PztS+rd5viLVk5Sfj2CqvHR5l3WjlFuH/RoB0DLz761cpLy2//uy7z46zprx/hXfhzch+iredaOUW71qjhZOwIgz7EVQgghhBDigfLfGRoTQgghhBBm8bA8x/bh2AshhBBCCFHhyYitEEIIIUQFpygPx+O+pGMrhBBCCFHRyVQEIYQQQgghHhwyYiuEEEIIUcEpysMx1vlw7IUQQgghhKjwZMRWCCGEEKKCkz/QIIQQQgghxANERmyFEEIIISo4RWWdx30ZDAY+/PBDYmJisLW1ZcqUKdSoUcNU/sMPP7B+/XoUReHVV1+lW7du/7g96dgKIYQQQlR0Vrp5bOvWrWi1WpYvX86xY8f49NNPmT9/PgA5OTksWrSIiIgICgoK6Nev3z07tjIVQQghhBBCWMXhw4dp3749AE2aNOHUqVOmMgcHB3x9fSkoKKCgoKBcf0RCRmzNzGAwsGbBXC5dSEBjY8NTI0bi7lPNVL5n/R8cityCokCX8MHUC2uF0Whk6ouDcfctWa5GcAg9n3vRYpkVYGSX1gS5V0Wn1zNjy25Sr10vs4yLgx1fhT/Gi4t+R6fX42Rrw/ieHXCytUWjUvF11AGiL2VYLPO9VK3Xgsavf8Jfr3exdhQAjAYDO3/9iayUJFQaGzoNHY6Lp7ep/PiWjcQf3AuAf4PGhD7+JEX5+Wz9bi7FRUWoNBq6DHsNR5cqZs2pAON7dqSOlxs6vZ6P/vyL5OwcU/kTTUN4sml99AYD3+06zM64RLwrOzOlbxcURSGnoJD31mzFyc6Gz57oblov2MudOZH7WHXk9P3PrMCHj3WmrrcH2mI9E9ZuIenKNVP5gOYNCA9tiN5g4OsdB9h+LgEHGw0f9emMn6sLNmo1H6//ixMX0+jTKJgX2zTHYDSw6kg0Sw+euO95SzMYDHw59WPOn4vBxtaW0R98RDX/Gncs8/6I/9G6U2f6DAjHaDQysFtnqt24dFevUWOGvTXKrDlvz7Ny3hwuJsSjsbFh4Ftj8PC91cZFrfudA1s3gwKPDnyOBi1bs2XFEs4cPghAQV4uOdlXmPrrbxbJazQYOLhyMdkXk1FrbGg5cCiVPLxM5Wf/iiDxyAEAfOs1pGHPvhiNRn6fNMa0nHtAEE0ef9IieW+nAGO7t6O2pxtavZ5pG6NIuZpTZpkqDvZ8/2xfBv24Cq1eb7Wcz7ZoSPUqlSk2GPh533HSc/PLLFPJzpbx3dsycf0Oig0GnGxteKlNUxxsbMgt0vLz/uNcL9JaJK/BYOCb6Z9wIfYcNra2vD5+Ij7V/e9YZsroN2nRoROPPvEUebnX+eKD98nPy6NYp+OFkaOp27CxRfKag7ke97V8+XKWL19ueh0eHk54eLjpdW5uLs7OzqbXarWa4uJiNJqSLqqPjw+9e/dGr9fzyiuv3PP/Z/aOrV6v5+WXXyY2NpZRo0bRv3//e64TExNDTk4OlStXZsqUKQAcO3aMRo0aoVKpGDZsGJ06dTJz8vvj9P49FOt0jJj+BYkxZ/jzx295fsJHAOTlXGPvxnWM/GI+Oq2WmSNeIiS0JVmXU6kWVIsX3p9slcztatXAVq1mxPL1hHh78FrHFrz/xzZTeVgNX15qF4qro73pvaeb1+dI0iV+OxpNddfKvN+zE68s+cMa8e9Qd/AYavQcjL4g/94LW0jCscMU63T0f+8j0uJj2bviVx4d8TYAORnpxO3fTf/xk1GA36dPJrBZGBfPRlO1WnVaPzWI6KhIjm3+kzYDnjVrzkeCa2KnUTP059U0rObF6K5tGbVyIwBuTg4MDGvEoB9WYqfR8NPQ/uxLSObZlo2JiI5jxeHTjOjUkn5NQlh26CTDF60FoFE1L0Y80pLVR6PNkrlb3SDsNBrCv1tOYz9vxvXowGtL1wHg7uzIkFZNeGLBUuw0apYOH8Du+CSGtwvlXHoWY1dHEOzlTl1vd05cTOPdHh3o/dUv5Gt1bBjxHOtPxpBTWGSW3AC7I7eh1Rbx5aIlRJ84zjczZzB5zldllvnpqy+5fu1WRz01OYnaISF8PPdrs+X6Jyf37kKn0zJ61lcknI1mzffzeXlSSbude+0au9av5d2vvkOn1TLt1Reo36IV3QYMotuAQQB888F4Hn/hZYvlTTl5FL1OR4/RE8hMiOfImhV0fPmNkryZGVw4tI/ub7+PAmyZ8xl+jZqhsbXF1a8GnV5502I5/07HOgHYatQMX7yWBr6evNW5Fe+sjjCVtwz04/WOLajq5GDFlNC0ujc2KjXTInZT060K4c3qMzfqoKm8vo8HTzUJobKDnem93vVrE5txhfWn46jn7c6TTery837znkzetH/HX+i0Wj77YSExJ0/w05zZjP98dpllliyYR27OrZOIP5YsplFoC/oMHMzFxAvMmjiemb8ssUje/5LbO7K3c3Z2Ji8vz/TaYDCYOrVRUVGkp6ezbVtJH2TYsGE0a9aMRo0a/e32zD4VISMjg+zsbKKiosrVqQWIiIggLi6O4OBgFi1axKJFi/Dw8ODHH39k0aJF/5lOLcCF6NMENw0FSkZeU+JiTWVOlV0YOWcBao2G61ezcXB2RlEUUuJiuZaVxYIJ7/DD5PdJT0m2aOaGvp4cuHARgDOXM6jj5Vam3GCEMb9t5nqpL/iVR6JZdyIGALVKZbVRgrvJvRjP7nFPWztGGZdjY/BvUHJm7xVUm/TEBFOZk2tVer31LiqVCkWlwqDXo9bY4FatOrrCQgB0hQWo1Oa/4NK0uje745MAOHkxjfo+HqayBr5eHEu+jE5vILdIS/KVa9TxdCcmLZPK9iUnPU52NhQbDGW2+W6P9kzdEIXBaDRL5uY1qrEz9gIAx1Mu07DardG4RtW8OZKUik6vJ7dIS1LWVep6udOuVg10egM/PNef1zq2YGdcIgAxaZlUsrPDVqNBUcA8iW85ffQIYW3aASUjr+dOlx3RjtqyGZVKIaxde9N7sdHRZKanM2bY84x//VWSLyRgSfGnTxHSPAyAwLr1SI6NMZU5u7jw7rzvUWs05GRfwcHJucylxOO7o3Bwdjatbwnp8bH4hDQAwD0wiCvJF0xljq6udPrfqLJ1z8aGK8mJFFzLZuuX0/lrwRfkpF22WN7bNfbzZl9CCgCnUtOp6+1RptxoNDJi2XqumfEErDxqe1Tl1KV0AM5nXSXAzaVMudFo5PNte8kr0pne83Vx5mRqyTqxGVeo7VH2u8eczhw/RtNWbQAIbtiI+LNlT7z3bNuKolLRrHUb03uPD3yW7v1LRu71ej02trYWy2sOyo3j/n7/3EuzZs2IiooCSgYx69SpYypzcXHB3t4eW1tb7OzsqFSpEjk5OX+3KcACI7YTJ07kwoULTJo0iZCQEGrWrMnnn3+OjY0NAwYMICEhgX379mEwGOjduzc9e/ZkzZo12NjYUL9+/b/tlZ88eZKpU6ei0Wiws7NjypQpfPfdd7Rp04auXbsydOhQunbtypAhQxg3bhyDBg0iLy+POXPmoNFoqFGjBh999BFr1qxh7dq16PV6Ro4cScuWLe/r/hfm52Pv5GR6rVKp0Ov1qNUlz4tTq9XsXr+WLUsX0faxfgBUrupG56fCadS2AwnRp1g2ezpvzpx7X3P9E0dbW/K0ty7/GAxGVIpi6ogcTkq9Y528G5eLXB0dGP9oB+Zt32+ZsOWQsn0Njt417r2gBWkLC7B1uDWiorrxJapSq1FrNDhUqoTRaGTfqiW4+wdQxduHYp2WlOiTLJ/0DoV5efQdO8nsOZ3sbMktdSlQbzSiVhT0RuONsltfnnlaHc72tqTl5PLmI63o2aA2Nmo1C0qN0nSsHcD5zCskXrlqtszOdrZlLl/qDQbUKgW9wVhSVnirLE+rpZK9La6O9rjY2zHslzX0axzCuB7tGbs6gti0TFa/OogCnY6I6LgyJ3PmkJeXh1OlSqbXKrUKfXExao2GhNhYIjdsYNLM2Sz+Zr5pmaoeHjwz7CU6du/BqSOH+XT8u8xbssKsOUsrzM/HwbF0G6e+o42LWreGDYsX0vHxsoMbW1YsZei771ssK0BxYSG2Do6m10qpuqdSa7B3Lql7R9euoKqfP5U9vSnMuUb9br3wbxpGenwsexZ9x6NjJlo0901OtmXrpKFUnQRMgxLW5mCjoUBXbHptMFLmeyT6cuYd6yRl59DEz9v0X1uN5Z6rWpCXh2Opy+EqldpU9xLj44iK2MjYT2aw4odvTcvcrKvZWZl88cH7vDhqjMXymkU55q+aQ7du3di9ezfPPPMMRqORadOm8dNPP+Hv70+XLl3Ys2cPAwYMQKVS0axZM9q2bfuP2zN7x/aDDz5g9OjReHjcOqssKipi5cqVAHTs2JHFixfj5eXF6tWr8fLyon///ri7u//jUPPEiRP57LPPCA4OZvPmzUyfPp0BAwawceNG2rRpQ15eHrt372bw4MHExMRQv359evbsybJly6hatSozZ85k7dqSS6Ourq7MnWuejqO9oyNFpS6BG41GU4N/U9vefWnZvRc/Tn6fuBPH8A+ui+rGg5ID6zXgWlYmRqOxXJOm74d8rRZHWxvT69KN0T8JdHNlUu+OzI86yPGLaeaM+J9na++A9sboK5TM+1OVOi6KdVq2//wtNvYOtB/8AgCH162mSY/HqNexC1kpSUTM/4IBH35q1px5RVqcbjsWbn6BlpTdGqFwsrXhemERE3t1YtK6SPaeT6Z9rRpMebwrbyxfD0DvhnVYcsC8lxZz75bZYLxVZnerzMnWlpzCIq7mF7Lt7HkAImPO83L7UIK93OlUJ5DOs38kX6vj8ycf5dH6tdl0OhZzcXJyoqDUJTmjwYj6xiW5rX+uJSs9jXdeepG01ItoNDZ4V/OlYbNQ1Dc6AA2aNSczPd2i7YW9oyOFBQWm1waD4Y42rkOf/rR59DEWTBrHueNHqdO4KZeSLuDg5FxmPq4laOztTVc+oOQzLl339Dod+5b8hI2dPaE3pvpU9Q8wPbzeM6g2BVezLfoZl5Z3R/uMqU4+SAp0xdiX6pgqCvf8HtlwOpZBoQ0Y07kVJy+lcyW/4B+Xv58cnJwozC9d9wymurd9w59cychg0uuvkH4pFY3GBk8fH5q1bktiXCwz33+PoW+OokGz5hbL+zBRqVRMnlx26mVQUJDp32+++SZvvln+aUBWeSpCYGCg6d+zZs1i1qxZDBs27J7Dy6VlZmYSHBwMQFhYGHFxcbRo0YITJ06wd+9eevfuTXp6OgcOHKB58+ZkZmaSmZnJW2+9xZAhQ9i7dy+pqal35LnfAkLqcfbGTRKJMWfwrhFgKktPSeaXTyaXdHY1GtQ2NigqFVuXLWbXujUApCbE4+rhadEG9FRqOi0D/AAI8fbgfGb2PdepUdWFDx/rxJQNOx6YEYMHmXetOiSdPAZAWnwsVf2qm8qMRiObvpqFm18NOg4ZhurGpRw7RyfTSJNDpcpoC83f6B9LuUy7WiWj3Q2reRGbnmUqO5WaRlN/H2zVapztbAl0dyUu/Qo5hUWmEaX063lUtr81hy7Ex4NjKea9jHs4KZWOdUrqdGM/b86Vynzi4mVCa1TDVlOSOcijKufSs26sEwBAWEA1YtOzuF5YRGFxMUXFxRiMRrLy8nEptS/mUL9pU/bvKrkkF33iOIG1a5vKXho1hrm/LmPmDz/T/fF+PDlkKGFt27NowdesXrwIgPiYs3h6+1i0vahZrwHRh0qu0CScjcY3oKapLC0lie+nTDK1cZobbRzAuaNHCAltYbGcN3nUrEVqdMnJVWZCPFVKdayNRiM7vpuLq68fLZ55zlT3Tm78g5jtWwDIvpiMo6ubVTq1ACdS0mhTs6S9aODrSVzGFavkuJe4jGwa+pZMA6rpVoWLV6/fYw2o4+nGnoQUPo/cR2ZugUX3LaRREw7v2Q1AzMkT+NeqZSob+sZIpv/4C1Pmf0fn3n14fNCzNGvdluTz55kx/l1GTZ5G8zb/PIr4X6Co1Gb5sTSrPBXhZmOh1WrZtGkTs2bNwmg00rt3b3r37o2iKBhum5d3O3d3d2JjY6lduzYHDhwgICAAtVpNSEgIP//8MxMnTuTixYvMmjWLMWPG4O7ujpeXF/Pnz8fZ2ZmtW7dSuXJlEhMTzdpA1W/VlnPHjjBv7EiMwIA3RxO19jfcvH2p37I1PoE1mTd2JCgKwc1CCWrQCJ+AQJbNms6ZQwdQq9UMeOtts+W7m51xiTSv4cvc8N4owGcRu3i6WX0uXs1hz/m7z/d9qV1oyQ1nnUqmcuRpdWVuOBNlBTYNJSX6JGs+/RCMRjo9/wrHIzbg4umF0WDg0rmz6IuLSTp1HICWT4QT1u9pti/8jtPbt2DQ6+n43HCz54w8e55WgdVZOPQJUOCDdZE827IxyVeusSP2AksPnuCnof1RFPhq+360ej2fbd7JuEfbo1IUFEVh2qaSjpqroz35pebTmcuWM3G0DfJn2fABKIrCe2sieKFNUxKzrhEZc55F+46xdNjTKIrCrG170BbrWRB1gKl9u7H8pXCK9XrGro4g9dp1lh08ydJhA9Dp9SRducbqY+a54e2mtp27cnjvXt56bjBGo5Exk6ew6pef8fX3p02nzndd55lhw/l0/Dj274xCrVbzzsdTzZrxdo3atCPm6GFmvT0CjDB41FgiV6/Ew9eXhq3aUi0wiFmjR6AoCiGhLah9467xtJRk6ja1/AhX9UbNuBwTTcSsaRgx0mrwi5yJ3Ewlj5K6lx4Xg6G4mNQzJY8catznCep368WeX74j9fQJFLWKVs9a7ik1t9t+LoEWAdX47tnHUVD4eMN2BoY1JCU7xzQ3/EFwJPkS9XzcGd+9pMP3477jdK9bk/TreRz7myt6l3NyGd6mKQDZ+YX8tO+4xfK27PQIxw7sY9zw5zEajbwx8UPWLlmMj191WnToeNd1Fn09F622iB9mzwDA0cn5jhvOhOUpRqN5r2GkpKQwevRo2rdvj7u7OzVr1mTZsmXMnl3yy//qq6+IiIjAxcWF4OBgJkyYwI4dO5g+fTqTJk2iVatWAHTu3JmNGzdiZ1cyYnLq1CmmTZsGgEajYdq0afj5+REZGcmkSZPYtWsX27dv57333mPXrl2o1Wp27NjBggULMBqNODs7M336dLZt20ZKSgqjRpXv8Thrz164/x+SmfStGwDAI7N/sm6Qf+GvUS+wvPV/5yl04XtL5pDNjjpk5STlN6pDyc2MTaZY5y76/4tj779GnUlfWDtGuZ2bPBKApMLieyz54PC3L6l3m+P/O1dcegRV46PNu6wdo9w+6FFyY2DLz769x5IPjv3vvsyLv66zdox/5cfBfYi+mnfvBR8Q9ao43XshCzgx+y2zbLfRqDlm2e7fMXsPws/PjxUryt7IUPoGrREjRjBixIgy5Z06dbrjyQeRkZFlXjdo0IAlS+58rEbnzp3p3LmzaTt79+41lXXs2JGOHcueeT399IN1t7wQQgghhKWZ6zm2lvZw7IUQQgghhKjw/jvXfIUQQgghhHmU45mz/wUPx14IIYQQQogKT0ZshRBCCCEqOGs9wu5+kxFbIYQQQgjxUJARWyGEEEKICs4af0zBHKRjK4QQQghRwSly85gQQgghhBAPDhmxFUIIIYSo6OQPNAghhBBCCPHgkBFbIYQQQogK7mF53Jd0bIUQQgghKriH5eYxxWg0Gq0dQgghhBBCWM/Z7yaZZbt1X5pslu3+HRmx/ZeS1n5r7Qjl5t/3ZQCaTp1v5STld3TC/5gddcjaMcptVIdQAJa3/u9UpfC9xQCkFOmtnKT8/OzU/7njGKDLnIVWTlJ+294a+v/Yu+/wKKqFj+Pf2ZZOCElISEJNCKEkoYQikFAVMEpTioiCgljAi4BKU0AUBcVgQRFBpPeXgIAiIL0jEDohjRQCaSSkb533j8VNIqh4b3ajcD7Pw8POnJnZ32xmZs+cOTMLwPkvJlRxkvsX/J9POfvp61Ud476FTvgSgIu5hVWcC+nkzQAAIABJREFU5P41dXPm2I2cqo7xt7Sr5c7oDTuqOsZ9+2pAz6qOADw4z7F9MNqdBUEQBEEQhIfev6eZSRAEQRAEQbCOB+RxX6JiKwiCIAiC8JCTFA/GUxEejOq5IAiCIAiC8NATLbaCIAiCIAgPOekB6YrwYKyFIAiCIAiC8NATLbaCIAiCIAgPuwfkBxoejLUQBEEQBEEQHnqixVYQBEEQBOEh96D0sRUVW0EQBEEQhIecJLoiCIIgCIIgCMI/h2ixFQRBEARBeMiJFltBEARBEARB+AcRLbY2ZDLJfBG9m8QbWahVSsY//Ri+Hm4AxKdnsuCHvZZpL6fc4L1hfWjdqL5NsknAlF4RBNZ0R2c0MnP7PlJz8y3l/Zo35umWTTCYZBYfOsXB+GR8XF14v3dXQOLG7QI++HE/pQYDAG6O9iwd1o8B365HZzRaPb9sMnFw1ffkpKWgUKnpPGwkrjW9LeVnd/1EwsmjANRpFkpY76fQFheze9GXGLRaFCoV3Ua8hqNrdatnvV81mrQhdPRH7B3draqjAGAymfh81kwSYmPRaDRMmDET3zp175pmyuhX6NClK08OHIwsywx6tAt+d6ZrEhrKyLHjrZqzsrdlCfhycCT7riax8fQlq2cf27Ud/h5u6IwmPt19hPTbBRWmcXWw44uBjzNy5Rb0RhP2KhVTeoZTzd6OEr2B2TsPcrtEa9Wcv0/t06U/Dh4+mIwGrv+yHt3tHADsPXyoFdHHMqWjd12St3+PS50g7D19AFA5VsOkKyFh/Rc2zezbfSAOnr7IRgOpO1ejy8s2Z/b0xbfLU2WZa9Xj2pZFaG9lUrvXUEBCn3+L1F1rkA16m6Q1mUx8+8lsrsVdRa3W8NqUd6lVu/Zd08waP5Y2EZ3o0f9pigoL+Hz6uxQXF2HQ63lh7HgaBYfYJO9veZbPm0tKQhxqtYYX35qMl5+fpXx39P9xaMd2kCT6Pv8izdt3oDA/n4Wz3qOkqAhn12q8+OYkqrnVsEleCRjUsgm+1athMJpY/esFsoqKLeVdGtYlrHYtAC7ezOLHSwmWslCfmrSo7c3S4+dsktVqHpCbx/7ntTAajYwYMYKIiAiio6Pva57Y2FhOnjwJwKRJkzhw4MAfTnuv8qysLGbMmAFA165d0WorHsQPHDjAunXrAFi3bh16vW0OPn/l8MV4dAYjX4wZwohe4Szctt9SFuBTk09fGcSnrwyid/vmdGzW0GaVWoAujeqjUSoZtiyaL/YcZ3z39pYydycHnmkdzPBl0Yxes43Xu7RFrVQwrtsjbDh9iRErNnMqJZ2hbc0HzUca1ObrZ56ghpOjzfInxZzCoNfTb/J7tOs/iKPrV1nK8rMyiT9+mL6TZtBv0gxSL50nJy2F2CMHqOFbmz5vT8M/rB0xP2+zWd6/EvTsm7SeshClxr6qo1gc3vMLOq2O+SvXMHLseL6Z+/Fd0yz58nMKbt+2DKenptAwqAlRS5YRtWSZ1Su1ULnbMsDozm2pZm9n9dwAHfzroFEqeX39Tyw+fIpXwsMqlIfV8WFO30dxcyjbLh5v1pC4zBze2LiDfVeTGNradpUXgGr+zVAo1SRs+JKbR7ZTK7y3paw0O52kTQtI2rSAnHOHyU84T2FyLDcObjGP37wQk66EtF/W2zSza0AICqWa+DVR3Dj4Az6d+pVlzrpOwvovSFj/BdkxB7gdd5aCa5ep1akPOWcPkbDuMwrT4vBs1dVmeU/s34deq2X24qUMHf06S7+Yd9c0qxd+TWF+2b63dc0qglu34YMFi3j93Rl8+8lsm+UFOH3oAHqdjmlfL2LAqFdZs6DsxKUgL49ftmzina++ZWLUFyyb9wmyLLN15TICg0N4Z/43PNpvABsWLbRZ3hBfL9RKJZ/uOcaW87H0D21kKXN3cqB1HR/m7jnG3D3HCPLywMfVGYCnmwfROzgQCclmWa1FUiis8s/W/ud3zMrKIjc3lwMHDtCvX7+/ngHYuXMn8fHx//V7enp6Wiq29xIREcGgQYMAWLhwISaT6b9+r8p08dp1WjeqB0CTuj5cTcu4a5oSnZ7lO48wuk8Xm2ZrUbsWRxJTATifnkGTWp6WsmY+XpxNu4neaKJQqyM19zYNa7rTwMONwwnJAMSk3qD5nbNZkyzzyuqt5JeU2iz/zbhY6jQLBcDLvyGZyUmWMie3Gjw+diKKOzuZyWhEqVLj7lsbfak5o760BIXyn3MBo/B6AocnDajqGBWcP3Oa1h06AuaW19hLFyuU79/5MwqFgjYdwy3jrl66RHZmBuNHDGfyay+TmpSEtVXmttw9qAEmWeZwQorVcwME+9TkZPJ1AC7fzKaRl0eFchmZt6N3UaDVWcZtirnMqpPnAajp4kSuDfc7ACef+hQkXwGg5GYKDjVr3zWNpNLg1a4H6fs3VxjvEdqRwpSraHNu2iTrb5x8G1Bwzdz6XnzjGo5ede6aRqHS4N3+ca7v3QiAvbs3BUnmeYquJ+Lk28BmeS+fjaHFI+YTtEbNgkm4UvHKwZE9u1FIClo80sEy7snBz/JY3/6AuQFKY2ebk7PfXD1/luA2bQEIaNqMpNgrljKX6tX5YPEyVCoVt2/dwtHZGUmSSE++RkjbdgA0DA4h7vxZm+X193Dj0s0sAK7duk2dGq6WstziUr46+CsyIANKhYTBaK5XJGbnsdbKV3KEv+d/rti+++67XLt2jWnTprFmzRqOHz/OgAEDGDJkCJs3b2bevHkMGjSIAQMGsHTpUjIyMoiOjmbp0qWcO1fWbH/8+HFGjhzJq6++ypNPPsmCBQssZevWreP555+nf//+nDt3jrS0NAYOHGgpnzZtGkOHDmXs2LGUlpayadMm5s6dy4YNG8jKymLcuHEAzJ49mwEDBjBgwACWLVsGmFuEX3nlFQYPHsztci1N1lBUqsWpXMuPQiFhNFasdO84cZ6IkEBcbdjaCeBkp6Gw3Jel0SSjlKQ7ZWoKSsvKirV6XOw0xGbk0LmhuVW5U2A9HNRqAI4npdn4UijoSkvQODhYhhV3KrAASpUKBxcXZFnm6IZVeNSpR3XvWtg5O5N26Tzrpr1FzM/bCerY2aaZ/0zavmhMNrrMeb+KCwtxcna2DCsVCox3LtcnxcWx56ftDB/9eoV53D08GTLyJaK+W8qQkaP4aMpEq+esrG3Z37MGPZs2ZMH+E1bP/BtHjZoibdnf3SibUEhlLUGnUm6QX3r3vmWSZeb2f4y+oY05npRmk6y/UWjsMerKKtOybLrrkmaNpm24HXcOY2mRZZykUFKj2SNknd5nq6gWCjt7jNq/yBz8CHlXYzCWmDOXZF6nmn8wANX8g1GobVdRLC4qxNGpbN9TlNv3khPiOfjzDgaPeqXCPE4uLtjZ25Obk83nM95l6KtjbJYXoKSoGAfn8pmVlsxgPi7v2rSRma+9RFgnc0NOnYCGnD58CIAzhw/edTXWmuxVSkr0ZflMsmzZ90yyTJHOvF/2C2lEWm4BmYXmbgqn02x7UmZNkiRZ5Z+t/c8V2+nTpxMQEICnZ1mriFarZfXq1fTt25fNmzczd+5cVq1ahb29PV5eXvTr14/hw4cTElLxkll6ejpffvkl69atY/HixZbxTZs2Zfny5QwdOpRNmzbdleGZZ55h5cqV+Pr6sn592SWtAQMG4Onpybx589i7dy9paWmsX7+e1atXs23bNmJjYwFo164da9euxdXV9a5lVyYneztKyn3hyrKMUlnxT7DnzGV6tQm2ao57KdLqcNSoLcMKScIoy3fK9DjZlZU52qkp0OqI+uUInQLrMX9wJCYZ8kpKbJ77Nxp7B3Sl5b6oTCYUSqVl2KDX8cvir9CVlhL+7AsAnNq6ieY9nmDQzE94Ytwkdi74zOa5/00cnZ0pKS6rmJhMMkqVuZV759YtZGdk8ObIF/j5h81sWLGME4cOEti0Ke27mC/ZBrdsRXZmBvKd7cpaKmtbfiI4kJouTnw7tDe9QxoxtE0o7Rvc3RpZmYp1ehw0ZVcOFEiY7vPzenPTTt7Y+BMzIm17tcekK0WpKavkSZIEcsUT9uqNWpJ78XiFcc61G1J0PRGTzrYtzAAmbSmKcpm5V+bGYdw6f8QynL4/mmr+wdTv/yrIMsaSQlvFxdHpj/e9/T9u51ZWJtNHv8Le7Vv5Yc0qTh81506Oj2PGmFd59pXRNG3ZymZ5ARycHCktLuujKptMlsy/ebT/03zxf1u5ei6Gy2dO8cSzz5F98wZzxv+HnMxMatSsabO8pQYj9uXySb/b91QKBcPbhmCvUrH29MV7LUL4h7DKtdf69cv6hkZFRREVFUV2djbh4eF/MhcEBgaiUqlQqVTY25f1IWvatCkAHh4elJZWPAiq1WqaN28OQMuWLTl8+DDBwXdXDBMSEggLC0OSJNRqNaGhoSQkJNyV15qa1vPh6KVEOoU24lJyOvW9K15mLCrRojMYqVm9mk3ylBeTepOIhnXZdTmBYB8v4rNuWcoupGcwunMbNEolGpWS+u5uxGfe4rEm/iw8eJK4zFs81zaUYzZuKSrPOyCQa2dPE9C6HRkJcdTwK6uAyLLMjvlR+AY1pUWvJy3j7Ryd0DiYW8YdXKqhK626ivm/QbPmLTi6fx+de/Ti0tmz1G/Y0FL28vg3La+XfT2fGh4etOkYzrfzPqWaa3UGvziChNgr1PSuZfUz+MraljecKvvyejk8jJyiYksXB2u5cCOTR+rXZn9cMo29PUjKyf3LeZ4Ja0ZWYTG7ryRSqjdgkm3b9aooPYlq9ZtyO+4sDt51KM2+UaFcobFHUqrQF+ZVGO9cJ9DShcHWitITqdYgmNtXz+BYq949MyuUKvQFZZld6gaRcfQnSrPT8WzVlYLkWJvlDQoJ5ddDB+jQ/TFiL5ynrn+Apez518daXq9dtBA3d3daPtKe1KRE5k6dyPgPZlO/YaDNsv6mYbMQYo4cpm2XbsRfvIBfA39L2Y2UZDYs+obXZ36IUqVCpdYgSQpiz8bQsUcvGrdoxcn9ewlsZrv+4onZuQT71OR02k3q1XC966bNlzu05GpmDrtird+dqqpICuVfT/QvYJWKreJOZ2GdTseOHTuIiopClmUiIyOJjIxEkqR79nv9oy+8P/si1Ov1XL58mcaNG/Prr7/SsNyX7W/zmkwm/P392bRpE8OHD0ev13PmzBlLn2BbNZV3aNqQU1eTGfvVamQZ3hzYg40HfsXHvTrtmwaQlp2Ldw3bV2oB9sQm0q6BH0uH9UMCpm/by9A2IaTm5rM/7hprTp5nyfN9kSSJr/adQGc0ci0njxlPdEFnMJKQncvsHQerJDtA/RZhpF06T/TsGSDLdB7+Mmd3/ohrTS9kk4kbV69gNBhIuWDus9W2/yBa9x3AvmWLuLhvFyajkU7Pj6yy/P8GHbt159SxI7z+3BBkWebt92exYflSfGvXsbTK/t4zI0by0eSJHD+4H6VSydsffGj1nP/mbflQfAqt6vjwxYBeSBJ8vOswT7dowvW8Ao4m3btSveNSPG8/2pFeTRuilCQ+3nXYppnzEy7gXCeQBgNeRwLSdq/Do0UE2rwcCpIuYlfdE13+3RV0TXVPci//atOsv7kddw7nukEEPDMOkEj9eRUerbqgy8siP+ECdm410eXfqjCP9lYGtXs8i2w0UJpzw6Y3vLXt3IWzJ48z+aUXkGWZMe9M54fVK/H2q02biE73nGfl1/PRaXUsiZoLmK+4TP4kymaZW4V34uKvJ3l/9ChkWWbkxKnsWL+Gmr5+tOwQTm3/AN5/bRRIENL2EYKatyAjLY1vP5oJgJuHJyPenmKzvGevZxDk5c6ELm1Bklh58jxdG9Yjq7AIhSTR0NMNlUJBE2/z1ekfzl8l6VbeXyz1X+YBeY6tVe+W0Wg0uLq60qdPH1xdXenQoQM+Pj40a9aMjz/+GH9//79eyF9Qq9WsWLGC5ORkfHx8mDBhAlu3brWUh4WFMWrUKJYvX86JEycYNGgQer2enj17WlqCbUWhkHjjqUcrjKtT093yulFtb94b1temmX4jA7N+qvj0iWs5ZTttdMxlomMuVyi/kJ7Js0v+7w+XGfnVqj8sq2ySQkHEcyMqjHOr5WN5/dKCpfecL3Ls29aM9T8pvpnM7pc6/PWENqJQKBj37owK4+rUv/sGmmGvlfXlc6nmyodffWPtaBVYY1teeNA2FTAZ+GzPsQrjUnPvvjHl2e/LsuYWlzJ5y25rR/sTMul7K3522txMy+uSzFRStn9/11zJW7+zerI/JnN997oKY7S3ym7mLclI4dqWRRXKi28mE7fqE5uk+z2FQsErEytW8vzq3X2lcfBLL1te27ISey8KhYLhEyoeX33q1rO87jd8BP2GVzxme/n58e5X39oi3l1kuOsmsIyCsu4fb2za9YfzxmXdIi7r1h+WC7b1P1ds/fz8KvRrBWjbtq3l9ZgxYxgzpmKn9c6dO9O5c2fA3L/1XvMdPmxudZg9u+wRJREREURERABY3vPnn3++K1P//v0tr+fMmWN5PXHi3TeulF++IAiCIAjCw0gSz7EVBEEQBEEQhH+Of86DOwVBEARBEIQqURWP5rIGUbEVBEEQBEF4yFXFr4RZw4OxFoIgCIIgCMJDT7TYCoIgCIIgPOQelOfYihZbQRAEQRAE4YEgWmwFQRAEQRAeduJxX4IgCIIgCILwzyFabAVBEARBEB5ykkI87ksQBEEQBEF4AIhfHhMEQRAEQRCEfxDRYisIgiAIgvCwe0B+oEGSZVmu6hCCIAiCIAhC1bmxe41Vllur+zNWWe4fES22giAIgiAID7kHpY+tqNj+TedyCqo6wn0LcXcBIHDaZ1Wc5P5dnfkGzT/4uqpj3LeYd14DIE1rrOIk98/PzvzrMuse+ffs/oOOGv6V20XrjxZWcZL7d3LyywAYbiZWcZL7p/JuwNX80qqOcd8Cq9kDsO/VzlUb5G/ovGAf8YXaqo7xtwQ42zF0WXRVx7hvK4f1q+oIAEgPSFeEB2MtBEEQBEEQhIfev6fJRhAEQRAEQbAK0WIrCIIgCIIgCP8gosVWEARBEAThYSduHhMEQRAEQRAeBA/KUxEejLUQBEEQBEEQHnqixVYQBEEQBOEhJ24eEwRBEARBEIR/ENFiKwiCIAiC8JATLbaCIAiCIAiC8A8iWmwFQRAEQRAedg/IUxFExVYQBEEQBOEh96B0RRAVWyszmUwsnjuba3FxqDVqXpn8LrX8at81zUdvvkHr8Age6/c0RYWFfPneuxQXFWHQ6xn2n3E0Cg6xak5JghlPdCXI2xOdwcjULbtIuXXbUj6wVTMGhQVjNJn4ev8J9l1NwkGt4r0nu+Ln5opaqeT97Xs5dz2DJ0Ma8WL7VphkExtPX2LNyXPWyQxM6dWJQC939EYj723bS2puvqW8f4vGPNWiKUaTiUWHTnEwPhnvas580KcbkiSRX1LK5OjdONmpmdP/Mct8jbw8+HzPMTaevmiV3GD+m38+ayYJsbFoNBomzJiJb526d00zZfQrdOjSlScHDkaWZQY92gW/O9M1CQ1l5NjxVsv436jRpA2hoz9i7+huVZahsraLUoOByOBAhrVrQaFWyw/nYtkcc9mm6zGxRzgNvdzRG4x88NN+0sqtB0B1B3u+e74vzyzegM5otFm28kwmE+/P+4rY+EQ0GjXvvfUGdf18LOUffr6AMxcu4eToAMCXs6ajVCp5P2o+aTduojcYmDL2VUIaN7JZ3gVzZpEUdxW1WsPr70zHp3adu6Z5740xtOvUmV5PDaS0pJi570ymIP829vYOjJ85C1e3GjbJC4AkETh4HE5+/sgGPbErP6Ek67qluHb3QdRs3RXZJJOyYyXZZw+htHeiychpKDX2yEYDl7+fhS7/ls0im0wmvp49i6Srsag1Gv7z7ox7fs4zxo6mXacuPP70QMv41KQkxg97llW79qKxs7NJXgkY3q45ddxcMZiMLD5yhoyCogrTuNhpmP54JyZv+QW9yYSDWsWYiNbYqVUYjCYWHPyV26Vam+QV/phNqudGo5ERI0YQERFBdHT0fc0TGxvLyZMnAXjuued4+umnLf/PmzfPmnEr1ckD+9DpdHy46HueffV1ln9xd/a13y6gML+sErlt7SqCw1oz8+tvGf3OdL77dI7Vcz4a5I+dSsWgReuYu+sQk3pEWMo8nB15rl1zBi9ez4vLo5nwaAfUSiUjO4ZxNTOHId9t4J0tu6nv4QbAxB4RDF/2f+bp27ekmr11DkxdGjXATqVk2NJNfL7nGOO7d7CUuTs58EzrEIYv28Rra7bxn67tUCsVDG0bys5L8YxYvpmErFz6Nm9MTlEJI1dsYeSKLXyx5xiXb2ax6cwlq2T+zeE9v6DT6pi/cg0jx47nm7kf3zXNki8/p+B22XaRnppCw6AmRC1ZRtSSZf+4Sm3Qs2/SespClBr7Ks1RWdtFdQd7Rnduy8gVmxmxfDOPN2uIj6uLzdajc2B97FRKRizfzPx9x3mj6yMVytvV92P+4EhqODnYLNO9/HLoKFqdjtUL5jFu1At88vWiCuWX4uL59pMPWPr5xyz9/GNcnJ34fu1GAurXZcX8ubz31liupaTZLO+xfXvQaXXMXbKCYWPGsuSzT++aZuWC+RWOyT9Hb8I/qDFzFi0l/LGerPtu0V3zWJNHaEcUag1nPhlN4uZv8X/qVUuZysEZ3y79Of3xaM59+SYBA8YA4P1IT4quJxITNZbMX/dQ+9FBNs18dN8edFotny5dyfDXx7J43ty7plnx9ZcVjnEAxYWFLJ43F7VGbauoALSq44NaqeC9n/az9tRFhoQFVygP9qnJxEc74Fru+ywioC6pefl8sOMgx65dJ7JZQ5tmrmySpLDKP1uzyTtmZWWRm5vLgQMH6Nev333Ns3PnTuLj4y3Dc+bMYcWKFWzYsIETJ05w/vx5a8WtVJfPxtCirfkLKbBZMAlXKrb4HN2zG0mSaNGuvWXcE4OG0L1vfwBMRiNqjfXPWFvV9eVg3DUAzqbdJNjXy1IW4uvN6ZR09EYjhVodKTl5BHl50DGgLnqjie+e78drndpwMD4ZgNiMbFzs7NCoVEgSyFbK3KK2N4cTUgA4fz2DprU8LWXNfLyISb2J3miiUKsj9dZtAmt6EJuRTTV7c8XLyU6NwWSqsMyJPcKZ9eMBTLK1UpudP3Oa1h06AuaW19hLFVuH9+/8GYVCQZuO4ZZxVy9dIjszg/EjhjP5tZdJTUqyasa/q/B6AocnDajqGJW2Xfi5VSP2Zjb5pVpk4GJ6ZoX9wtpCa3tzJDEVgAvpmTQutx4AJllm9Npt5JdUbQvR6XMX6dimFQChTRtzMTbOUmYymUhJS2fG3C94dvQENm3/GYDDJ06hVqt46c2pfLNsNR3uzG8Ll86eoVV78/E2KDiEuMsV973Dv+xCUiho1b6jZVyfIUMZ+OJLAGTdvEF1d3eb5QVw9Q/m1qUTAOQnXcKlblnrtlFbQumtDJR29ig1Dsh3jl1F6Yko7R0BUNo7Idu4Rf9SzBlatTefVAYFhxJ/qWJjwaHdO5EUCsI6lH3Osizz5ayZDBvzH+zsbXvC1qimO+euZwCQkJ1LfY/qFcplWWb2rsMU6nSWcam5t7FXmy98O6hVGE3W/d4Q7o9NuiK8++67XLt2jWnTptG4cWMaNGjA3LlzUavVDBw4kKSkJI4dO4bJZCIyMpJevXoRHR2NWq2madOmFZal0+kwGAzUrFkTo9HItGnTuHnzJrm5uURERPDGG28wadIk8vLyyMvLY+HChSxYsIBTp04B8MQTTzBs2DAmTZqERqPh+vXrZGZmMnv27LveqzKUFBfh6OxsGVYoFRgNBpQqFSkJ8Rza9TMTZs1h45KyFgAnF3OrUG5ONl+89y7D35hQ6bl+z9lOQ4G2bIc1mkwoFRJGk2wuKy0rK9LpcLHX4OZoj6u9HSOWR9M3tDGTeoTz9qadxGVks+mVIZTo9ey8FE+BlS7NONlpKCyfWZZRShJGWb5TVva+RTo9zvYaMvIL+U+XdvRq1hC1Usk3B05apunUsB6J2bdIvpVnlbzlFRcW4lRuu1AqyraLpLg49vy0nemffsaKb762TOPu4cmQkS/R6bGenD99io+mTOTrNeutnvV+pe2LxtG77l9PaGWVtV2YZPD3rEENJweKtXra1PMj+dbte72lddZDo6ao3HqYTCbLegCcuHb9j2a1qaLiYlycnCzDCoUCg8GISqWkpLSUIf17M2xgP0wmEy+8MYmmQYHk3s4nv6CQRXNnsWXHbuZ+vZiPpr5pk7zFRUU4OpW1vCsUSsu+lxwfx/4dPzJpzqesXbywwnxKpZKpr47kWnw878//xiZZf6NycMJQUmgZlk0mJIUS2WSurGpzM2k9bRmSQkHKjlUA6AvzqdE4jNbTlqJ2rMaZT1+3aebfH+MU5Y5x1+Lj2LfjJ6Z8/ClrFpV9lqu/XUDrjuE0CLRNt5TyHNQqivUGy7DJJKOQJEsjx4UbWXfNU6jVEexTkzl9uuFkp+H9nw7YLK9VKKSqTlApbFKxnT59OuPHj8fTs6zFQavVsmHDBgA6derEypUr8fLyYtOmTXh5edGvXz88PDwICTH3LZ04cSIODg6kpqYSFBSEm5sbN27coHnz5gwYMACtVmup2AK0a9eO4cOHs3fvXtLS0li/fj0Gg4EhQ4bQrl07AHx8fJg5cybr169n3bp1zJw5s9LX3cHRiZLiYsuwbJJRqswf+/4d27mVlcl7r79C1o0bqNRqPGv50KJde5IT4vls2hSeHzOWpi2s35pRqNXhVO7Sj0KSLGefhVodTnZlZU4aDfmlWvKKS/nlSiIAe2ITGRUeRiMvDzoH1qfrvCUU6/TMfaonPZs2ZMfFOCpb0b0y/9ZaodXhpNGUy6ymoFTLu493ZtrWPRxNTCU8oC4f9O7O6+u2AxCMLw1bAAAgAElEQVQZHMjqE9bpD/x7js7OlBSX9d8yldsudm7dQnZGBm+OfIGb6ddRqdV4+fgS0ioMpUoJQHDLVmRnZiDLMpL0YByMKktlbhdzdx3i06d7kpFfyJWbWeQVl9huPXR6HMuth1RuPf5JnBwdKSr3uciyCdWd7dTezo7nnu6Dw53W8DYtQomNT6S6qwtdOpiPw507tGXxatudoDk6OVXY92TZZNn39vy4jZysTKa++hKZN9JRqVXUrOVraXmctWAxqdeSmPnGGBZt3m6zzIaSIpR2jpZhSVJYKrU1mrVFU82d4+88A0DI6x9zO+ECdXoMIWXnWm4c2oqTbwOajprJr7NG2Cyzo7MzJUVl332m8p/z9q3kZGYw5ZWRZKSno1Krqenjw94ft+Ph5cXOLdHk5mTzzuiX+XjxUpvkLdEbcFCVVYnKV2r/SL/Qxmy/EMeeq9eo7VaNsZ3bMmXrHmtHtRpJoazqCJWiym4eq1+/vuV1VFQUUVFRZGdnEx4efs/p58yZg7+/v/mGmilTWLx4Mc8//zznz5/n2LFjODs7oyt3ieC35SckJBAWFoYkSajVakJDQ0lISACgcePGAHh7e3P69GmrrGdQSCi/Hj5I+26PcvXCeer4B1jKnhs91vJ6/eKFVHd3p0W79qQmJRI1dSLj3v+Ieg0DrZLr906lpNO1UQN+uhhHqJ83VzNzLGXnrt9kXPf2aFRKNEol/p41uJqZw6mUdDoF1uPijUxa1/MlLjOHglItpQYDWoMBkyyTU1RcoU9SZYpJu0mnhvXYeTmBYF8v4splvpCewZgubdEolWhUSup7uBGfeYv8Uq2lNS+zoKhC/9/GtTyJSbtplay/16x5C47u30fnHr24dPYs9RuW9c16eXxZy9Wyr+dTw8ODNh3D+Xbep1Rzrc7gF0eQEHuFmt61RKX2Hipru1BKEiG+3ry4LBqlQsHCob35cu9xm63H2bSbRATUZfeVRJr51CQhy3Y3/vwdLYKbsO/IcXp2jeDsxcs0LHdsv5Z6nTdnzmbjoi8xyTJnzl+kb8/utAxuyoFjJ2naqCGnzl4goJ7tWvobh7bgxIH9hD/agyvnz1HXv2zfe+E/4yyvV3+7ADd3d1q178CG77/D3asmXR9/Ent7BxRK2/YbvJ14AY/g9mSd3ke1+k0oTE+0lBmKCzDptZgM5u3XUFKIytEZQ3EBxlJzBV5fkIfKwemey7aWJqHNOX5gP+GP9eDK+bPUCyj7nF8sd3/AqoVf4+buQVj7jizeUnay8MITPfngq4qt5tZ0NTOHFrW9OZ58HX8PN1Jz//rqTJFOR7FOD0B+iRYHjbgf/5+gyv4KijuPldDpdOzYsYOoqChkWSYyMpLIyEgkScL0u/6Pv83n5eWFXq9n06ZNuLi4MHPmTJKTk1m/fr2lf9FvX/j+/v5s2rSJ4cOHo9frOXPmjKWfry0qBW06deHcyeNMHfUisiwzeup0tq5ZibdfbVqHd7rnPKsXzEev0/H9Z+bO9o5Ozkz8OMqqOXddjqeDfx3WjhyIJElMjt7JC+1bkJxzmz2xiaw4FsOaEQOQJImoX46gMxj55sAJZvV5lHUvDcJgNPL2pp2k3y5g7cnzrBkxEL3RSMqt22yKsc6NWHuuJNKufm2WDesPEkzfuoehbUNJvXWb/XHXWHPyHN8P64ckwfx9x9EZjcz5+SCTeoajkCQkSeLDHeZLR26O9hRr9VbJeS8du3Xn1LEjvP7cEGRZ5u33Z7Fh+VJ8a9ehfZeu95znmREj+WjyRI4f3I9SqeTtDz60Wd5/k8raLoyyjN5oZM3IAWgNRlYciyGvpNRm67EvNom29fz47rk+IEnM3LaPIa2DScvN58Cd/uz/BN3D23P01zM8+9p4ZFnmg0njWbpuE3X8fOjaoR1PdO/CM6+OQ61S0btHNwLq1+WloYOZ/vFnDHl1HCqVio+m2KYbAsAjnbsSc/wob734PDIyY6fNZPOq5dTyq0PbTp3vvY69+/LZjHfYtWUzJpORsdMq/+ren8mOOUiNoDBavDkfJInY5XPw6zaAkqzr5Jw7QkHQFVq+/TWyLHM74Ty5l3+lKD2JRkPfwieiD5JSRezKT2ya+ZEu3Thz/BgTXngOZJk3pr9P9Mrl1Kpdm3adutg0y/34NSWdZj41mdYrAgmJbw+foleTADIKCjmdeu8Gj41nLjOyfQu6BzVAKSn47sgZG6euXFVxo5c1SLJs/WtbaWlpjB8/nvDwcDw8PGjQoAFr1661PN1g/vz57Ny5E1dXVxo1asTUqVPZv38/H3/8MdOmTeOrr76ipKQEBwdzZ3J7e3s++eQTsrKyGD9+PE5OTjg4OHDjxg2WLVvGvHnzePzxx4mIMN/ZP2fOHE6fPo1er6dnz56MGjWKSZMmWaY5cOAAP/74I7Nnz/7LdTmXU2C9D6qShbib+5EFTvusipPcv6sz36D5B1//9YT/EDHvvAZAmrZqHrX03/CzM19uWvfIv6d1YdBRw79yu2j9ke1anP5XJye/DIDhZuJfTPnPofJuwNV8251s/K8Cq5m7ZOx7tXPVBvkbOi/YR3zhv+sRVgHOdgxddn9PYPonWDns/m6qt7bbMfutslzX5vduxLMWm3yz+fn5sX59xT5Ubdu2tbweM2YMY8aMqVDeuXNnOnfuDGDpE/t71atXZ+vWrXeN/30FdeLEiX86TUREhKUSLAiCIAiC8LARP9AgCIIgCIIgPBgekHs2HozquSAIgiAIgvDQEy22giAIgiAIDznRFUEQBEEQBEEQ/gcmk4kZM2YQGxuLRqPhgw8+oG7dskcA7t+/n6+++gqAJk2aMH369D99qtWDUT0XBEEQBEEQ/muSpLDKv7+ye/dudDod69atY8KECRVu7i8sLOSTTz7hm2++Yf369fj6+pKbm/unyxMVW0EQBEEQBKFKnDp1yvLjXM2bN+fChQuWsjNnzhAYGMicOXMYMmQIHh4e1KhR40+XJ7oiCIIgCIIgPOSs9ZO669atY926dZbhQYMGMWjQIMtwYWEhzs7OlmGlUonBYEClUpGbm8vx48fZvHkzjo6OPPvsszRv3rzCr9f+nqjYCoIgCIIgPOwU1nnc1+8rsr/n7OxMUVGRZdhkMqFSmaun1atXJzg4GE9PTwDCwsK4fPnyn1ZsRVcEQRAEQRAEoUq0bNmSAwfMP28fExNDYGCgpaxZs2ZcvXqVW7duYTAYOHv2LAEBAX+6PNFiKwiCIAiC8JC7nxu9rOHRRx/l8OHDDB48GFmW+fDDD/n++++pU6cO3bp1Y8KECYwcORKAnj17Vqj43ouo2AqCIAiCIAhVQqFQMHPmzArj/P39La8jIyOJjIy87+WJiq0gCIIgCMJDrqpabCubJMuyXNUhBEEQBEEQhKpTknjOKst1aBBileX+kQejei4IgiAIgiA89ERXhL+p2+fLqjrCfftl7DAA+n+77i+m/OfYNGoQgdM+q+oY9+3qzDcAaDFrQRUnuX9npr4KQPMPvq7iJPcv5p3XWPfIv+dwNeioAQC7V6dVcZL7p11g7uP2xv/trOIk9++zpx4j4J15VR3jvsV/MA749+17BRePVHWMv8WlaXvqT/20qmPct6RZE6o6AgCS4sFo63ww1kIQBEEQBEF46P17mkAEQRAEQRAEq3hQbh4TFVtBEARBEISHnOiKIAiCIAiCIAj/IKLFVhAEQRAE4WH3gHRFeDDWQhAEQRAEQXjoiRZbQRAEQRCEh5ykkKo6QqUQLbaCIAiCIAjCA0G02AqCIAiCIDzkJIWyqiNUClGxFQRBEARBeMg9KM+xfTDWQhAEQRAEQXjoiRZbQRAEQRCEh534gQZBEARBEARB+OcQLbZWJgFju7bD38MNndHEp7uPkH67oMI0rg52fDHwcUau3ILeaMJepWJKz3Cq2dtRojcwe+dBbpdobZp5VMdW1HOvjt5o4usDJ7mZX1hhmmr2dnzYpxvjNu5AbzShkCSGt2tOgGcNVEoF605d4FTKDevmlGDGE10J8vZEZzAydcsuUm7dtpQPbNWMQWHBGE0mvt5/gn1Xk3BQq3jvya74ubmiVip5f/tezl3P4MmQRrzYvhUm2cTG05dYc/KcdTIDU3pFEFjTHZ3RyMzt+0jNzbeU92vemKdbNsFgkll86BQH45PxcXXh/d5dAYkbtwv44Mf9lBoMluV9OTiSfVeT2Hj6kpXydiLQyx290ch72/ZWyNu/RWOeatEUo8nEojt5vas580GfbkiSRH5JKZOjd1NqMBAZHMiwdi0o1Gr54Vwsm2MuV3rev6tGkzaEjv6IvaO72fy9JUniy8FPEOznjdZg4NWVW0jIumUpf7FDK0aGh2EwmZj9435+vHAVdydHlr/4NPYaNTfy8nlp+WZK9Hp6NG3I1MjOAMSk3OA/a7fh5ujA0heewsXejltFxby66geyCooqfz2Ap1s0xtfVBYPJxNpTF8kuKrGUdwqoQ8vatQC4dDOLny8nYq9SMbRNMPYqJSqFgs3nYrlWbt+1BkmC957sRmNvD3RGI1Oid5Fc7j0HhTVjcOsQjCYTX+07zt5Y8/FiZu9ud44XCmZuMx8vgn29mNKrE5IE2QXFjN/4EzqD0XrZ+fv7oU91F97v3Q0JuHG7kPe377McN2zBZDIx+9sVxF1LRa1W8e5rL1C7lpel/PDpcyxatwWAoAZ1mTjqOSTJ/LipvcdOsfvoSWaNe8XqOSUJ3u/dncZ3vkcmRe8k+VaepXxwWDDPtAnBaDQxf99x9sQm4upgz55xL3I1IxuAny/FsfToGV4Ob03v0CAKS3UsPHiSPbGJVs9fmX77/P/tKrViazQaGTVqFHFxcYwbN45+/fr95TyxsbHk5+fTunVrnnvuOUpKSnBwcECv1+Pn58fUqVNxc3OrzJg21cG/DhqlktfX/0Rjbw9eCQ9j2ra9lvKwOj6M7NASNwd7y7jHmzUkLjOHFSfO0aOxP0Nbh/DVgZM2y9ymni9qpZLJW34hsKY7w9s1Z/bOQ5by5n7eDG0TQvVymTs1rItKoWDKD79Qw9GB9g1qA9at2D4a5I+dSsWgResI9fNmUo8IXluzFQAPZ0eea9ec/t+swU6lZM3IgRxOSGFkxzCuZubw9qadNPLyIMjbg3PXM5jYI4LI+csp1un5cczzbD8fS35p5Z9MdGlUH41SybBl0QT7eDG+e3vGbdgBgLuTA8+0DubZJRuxU6lY8nxfjiWlMq7bI2w4fYkdF+Po17wxQ9uGsPjwaQBGd25LNXu7Ss9ZlrcBdiolw5ZuItjXi/HdOzBuw0/l8oYw5LsN2KlUfD+sH8eSUhnaNpSdl+JZf+oiYzq3pW/zxuy4GMfozm0ZvGg9BaVaFg7tzYmktLtO8mwp6Nk3qdvrWYwlxVXy/r1Dg7BTq+j0ySLa1PdjzlM9ePqbNQB4VXNmdJd2PDL7G+xVKva+OZLdVxKYGtmZtSfPseJYDG8+Fs5L4WEsOXyKj/o/xqNR35NTVMyERzvi4ezIm4+FczghhY93HKBrUANm9unOqyu3VPp6BPvURK1Q8Nm+E9St4UqfkEZ8dzQGMG8jrerUYt6e4wC83qk1565nEurrRVxmDvvjU6jp7MhzbUL4dM+xSs9W3qONA7BTKRnw7Tqa+3kzuVcnXln1A2A+XjzfrgX9FqxGo1Ky7qVBHI5P4aXwMK5m5PDW//1MIy8PGtfy5Nz1DGb17c7ra7aRfOs2A1s1w7d6NZKyc62W/b/ZD8d1a8/GUxf56bfjRrtQFh86ZbWMv7fvxGl0ej3fz36H87EJzFu6lqjJYwEoKinh82Xr+fb9iVSv5sKy6B/Jyy/AzbUac79bxdGYCwTWq2OTnI/d2S6eWriG5rVrMfXxToy6s594ODsy7JEW9Pl6FXYqJetHDeZQfDLNfGqy9dwVZmzbY1lOIy8P+oQG0feb1QD838vPcCQxhVK97U4m/lfi5rF7yMrKIjc3lwMHDtxXpRZg586dxMfHW4bnzJnDihUrWLt2LREREUybNq0yI9pcsE9NTiZfB+DyzWwaeXlUKJeReTt6FwVanWXcppjLrDp5HoCaLk7klpTaLjDQ2NuTM2nmSunVzBz8PSueWMiyzIzt+ygsl7m5nzc5RcVM7RnOqxFhlnW2plZ1fTkYdw2As2k3CfYtaw0I8fXmdEo6eqORQq2OlJw8grw86BhQF73RxHfP9+O1Tm04GJ8MQGxGNi52dmhUKiQJZCtlblG7FkcSUwE4n55Bk1qelrJmPl6cTbuJ3miiUKsjNfc2DWu608DDjcMJ5pwxqTdofqf1q3tQA0yyzOGEFCulhRa1vS3LP389g6a/yxuTWi7vrdsE1vQgNiObavbmkx4nOzUGkwk/t2rE3swmv1SLDFxMz6zw96oKhdcTODxpQJW9fwf/uuy8FAfAiaQ0Wtb1tZS1rufL0YQUdAYj+aVaErJyCPb1or1/HXZeMh8vf754la5B/jzSoA4Xrmcw5+ke/DJhBBkFhWQXFtO4lic/X7gKwJGEFDr4W6ei0MCjOpczcgBIvnWb2m7VLGW5xaUsPHQaGfM+pVQoMJhM7I9P5khiGgAKScJgMlklW3lhdX04cOd4EZN2k2bltr9QP29OpaSju3O8SL6VRyNvD8ID6qI3Gvl+WD/GdGnLgbhr1PdwI6+4lOHtW7J6xABcHeytWqmF/24/bODhxqE788Sk3qTFneOGrcRcjuORFsEABDfy53LCNUvZuSvxBNT1Y97StYyc+iHu1avh5mrebkIaBTB51PM2yxlW15f9V83ZYlJvVDguNferZdkuCu5sF0HeHjTz8aKpT03WjhzIV4OfwNPFiQDPGhxLSkNnMKIzGLmWnUeQt+cfvKtgTZXaYvvuu+9y7do1pk2bRuPGjWnQoAFz585FrVYzcOBAkpKSOHbsGCaTicjISHr16kV0dDRqtZqmTZvetbzevXvz2WefodVqGTlyJG5ubuTn5/Pll1/yzjvvUFBQQG5uLgMGDGDIkCGcO3eO9957DycnJ9zd3bGzs2P27NksWbKE7du3o1KpCAsL46233uLLL78kLS2NnJwc0tPTmTx5MuHh4ZX5cQDgqFFTpNVbho2y+bK9STZXnf7ocr1Jlpnb/zHqu7vxdvTOSs/1Zxw1aop1ZZlNslwh89nrGXfNU83ejlquLszacZAmtTwZ07kN727de9d0lcnZTlPhhMBoMqFUSBhNsrmstKysSKfDxV6Dm6M9rvZ2jFgeTd/QxkzqEc7bm3YSl5HNpleGUKLXs/NSPAVWaK0FcLLTVDghMJpklJKEUZZxslNXyFys1eNipyE2I4fODeuz9XwsnQLr4aBW4+9Zg55NG/LW//3MqPAwq2S9Z165fF4Nhdqyz6lIp8fZXkNGfiH/6dKOXs0aolYq+ebASUwy+HvWoIaTA8VaPW3q+VW4DFwV0vZF4+hdt8re38XBjvxyXYzM268Co8mEi70dt8ud0BaU6nB1sKdaufGFWh3VHOxwd3akU2B92ny4gEKtjj0TRnA8MZWzaTd5IjTI/H9IEA4atVXWw06lqtAqJZc7XphkmaI7x5LewYFcz8snq7CshdzFTsPQNsFEn421Srbyfn9MMN11vCi3LWt1uNjb4ebogKuDPS8si6Zv88ZM7hnBmpPnaVnHh5nb9nItJ49Fz/XhQnoGR++csFrDf7MfXs3IpnNgPbae++24Ydueh0XFJTg7OliGFQoFBqMRlVJJXn4hpy5cZtWnM3G0t2PkOx8R3CiAuj7ePNaxLb9euGKznC72dhRoy++H8l9uF4nZt/jslwwOJ6TQJzSI957oyqe7D/Nqp7Y4adSolUpa1vGxWpc2a5HEzWN3mz59OgEBAXh6lp2laLVaVq9eTd++fdm8eTNz585l1apV2Nvb4+XlRb9+/Rg+fDghISH3XGa1atXIzzf3JXryySdZunQpKSkpREZGsmTJEr755huWLl1qef/Zs2ezfPly6tQxt07Exsby008/sXbtWtauXUtycjJ795orXBqNhsWLFzN16lTLMipbsU6Pg6bsgKKgrIL4V97ctJM3Nv7EjMguVsn2R4p1ehzUZV+C95O5QKvj15R0AC7dyMLH1cWqGcH8xe5U7staIZkPRpYyu7IyJ42G/FItecWl/HLF3O9pT2wizXy8aOTlQefA+nSdt4QuUUtwd3KkZ9OGVslcpNXh+PvMdz7bIq2+QmZHOzUFWh1RvxyhU2A95g+OxCRDXkkJTwQHUtPFiW+H9qZ3SCOGtgm90/2j8vPe9Rlb8upw0mgsZU4aNQWlWsZ1a8+0rXt4auFaPtl5iA96d6egVMvcXYf49OmezHiyC1duZpFXXHLX+z1MCkq0ONuVfX7m7dfccllQqsWlXBcTF3sNecWl5Jcb72yn4XZJKbeKijmVnE5GfiFFWh2H4q4R4ufNxzsOULdGdX4aO4zabq6kleuTWZm0BgN2qrIHu0u/O16oFAqea23uT7vhTFm/6lrVnHktIoztF+JJsHKLJ5iPCXd/3vI9y5zsNOSXaMkrKWX3lQQA9lxJJNjXi7ziEpJz8ojPuoXBZOJA3DWa+Vj36sN/sx9+uvsInRrW46tnnkCWZfKKbXvlz8nRgeJyJ2eySUalNG8nri7ONAmoj4ebK44O9rRsEsjVJOtdefozBaVanDV/vF04/X67KNVyJCHFciLz86V4mtSqSULWLZYfO8P3w55i6uOdOJt2g9yH/BhXVaxePa9fv77ldVRUFFFRUYwYMcJSWf0zsiyTnZ2Nu7t7hWV5eHiwe/du3nzzTRYsWIDhTof4zMxMGjY0V0hatWoFQGJiIqGhoajVaiRJIiwsjLg48+W/xo0bA+Dt7Y1Op8MaLtzIpG09P/P7eXuQlPPXB/BnwprRPagBAKV6AybZ+pfpyruSkW252SOwpvt9taxdvpllmadejepkF1q/3+KplHQ6BZq3iVA/b65m5ljKzl2/SVhdXzQqJc52Gvw9a3A1M+fOPPUA8+XeuMwcCkq1lBoMaA0GTLJMTlExrlbqtxqTepOOdy4JB/t4EV/uZqEL6Rm0qF0LjdKcub67G/GZt2hX34+FB08yZu12ZFnmWFIan+85xvNLN/HSyh/44VwsK0+ctXRxqNS8aTfpGGBu1Qy+0y+yQt465fJ6mPPml2otrUuZBUVUs7dDKUmE+Hrz4rJo3tnyC/U83IhJvVnpef9NjiSm0LNZIABt6vtxMT3TUnby2nU6BNTFTqWimr0dQd6eXEzP5GhCiuWkq0fTQA7FJ3M6JZ2mPjVxd3JEqVDQpn5tLt/MIrxhPVYdP0uvz5eRlJPLUSt1WUnMzqOJt7mLVd0artz43Y2mI9s3J/12AevPXLZ08fFycWJ4u1BWnDjH5Ts34FjbqeSyfb+5nzex5d73bNq9jhfZ/Jp8nc53jjFt7hwvUnNv42inpm4NV8B8Kbv8fmEN/81+2K5+bRYe/JXRa7ZhkmWOJVmvRfleQoMacvi0ucXyfGwCAXX9LGWN/euSkHKdvPwCDEYj568mUt/Px6b5fnMqJZ3Ojcx/4+a1a1XYLmLSbtD6znbhYqchwNOd2IxsZvfrQa87+2EH/zpcSM+ghqMDbo4ODFy0lpnb9lLL1aXCsv4NClTOVvlna1a/NqG407St0+nYsWMHUVFRyLJMZGQkkZGRSJKE6Q/6V23cuJF27dpZlvHbHXtLliyhefPmDBkyhGPHjrF//37AXEGNj48nICCAs2fPAtCgQQO+//57DAYDSqWSkydP0rdvX65cuWKTOwAPxafQqo4PXwzohSTBx7sO83SLJlzPK+DoHxxodlyK5+1HO9KraUOUksTHuw5bPWd5x5PSCPX14sPe3ZAkmL/vBE8GB3Izv5CTyen3nGfX5UReDm/F7D7dQYKFB61/k8Kuy/F08K/D2pEDkSSJydE7eaF9C5JzbrMnNpEVx2JYM2IAkiQR9csRdAYj3xw4waw+j7LupUEYjEbe3rST9NsFrD15njUjBqI3Gkm5dZtNMZX/hAEwtxK3a+DH0mH9kIDp2/YytE0Iqbn57I+7xpqT51nyfF8kSeKrfSfQGY1cy8ljxhNd0BmMJGTnMnvHQatku2feK4m0q1+bZcP6gwTTt+5haNtQUm/dvpP3HN8P63dnOzmOzmhkzs8HmdQzHIUkIUkSH+44gFGW0RuNrBk5AK3ByIpjMeTZuO/4P82WmMt0D/Jn35sjkSSJUcujGdutPQlZOWw7F8tXe4+xZ8IIFAqJaT/8gtZg4KOf9vPdsP682DGMnMIinl+ykWKdnne27GLbf8z9EjeeusCl9Ey0egPfDesPQHpePi9b4cYxgPPpmTTycmds5zZIwOpTF+jcsC5ZhcUoJAl/DzdUCgWN71R+t12Io1uj+qgVCvqHBgFQojdYbjizlp2X4+kQUJf1owYhARM37eTF9i1JvpXHL1cSWX7sDGtHDkQhSUTtOozOYGTB/hN82PdRNowahN5k4q2NP6M3mpgcvYuogY8jAadTbrDvapJVs/83++G1nFxmPNEFvdHI/7N333FV1X8cx193sRFQEGQKOFHEPXOUNs0sM0dmltowNVflytEwNTXNHJma5ShHzp+59zb3FhAQRBAHyobLHb8/rl64oUXlvRR+no8Hj7jn+z2H9z2d8T3f8z3HmBupjLfhcQPg8UZ1OXzqHD2Hf47RCGP69WLxus0E+JSnZcM69H2tI/0+nQJAm6YNLBq+trT5fDSPVQril7e7olDAhys306tZPeJv3WHbxRh+PHiC5W91QalQMHnrPrQ6PRO37OHLDk/zWqPaZOfnM2z1FlKzcwgs68aaPt3I1+sZv2l3se/OiodLYTQ+vDWfmJjI4MGDad68OZ6enoSEhLB06VKmTp0KwIwZM9iyZQtubm5UrVqVkSNHsnv3br788ktGjx7NzJkzzW9FAPD29mbMmB5Quy8AACAASURBVDG4urrSvXt3xo4dS2hoKIcOHWLs2LF4eHjg7u5OdHQ0GzZs4OLFi3z++ec4OTmh0Wjw9vbm888/Z8GCBWzYsAGDwUC9evUYPnw4M2bMwNPTk65duxITE8PYsWNZtGjRn37H1l//+LBWl9VtH9ADgA7fLSvhJMW36u3OVBk9raRjFFvUpwMBqDNudgknKb4TI/sAUPvzWSWcpPhOfvwey5r8d95O2Pmg6S6SfZ//zsOvebM/BWDgStuO6f8npr38FJU+nlrSMYrt0ueDgP/evpdx7kBJx/hLXGs0JXjklJKOUWxx44aUdAQAMjKs85YaV1frD00s7KGeKfz9/Vm+fLnFtEaNGpl/79evH/369bMob9WqFa1atQKgcePGD1x24UZn48aN2bRpU5E6Z86c4dtvv6Vs2bJMnToVzd1xom+++SZvvvmmRd3+/fubfw8NDS1Wo1YIIYQQQvx7/Xe6QIqhXLly9OzZEycnJ1xdXZkwYUJJRxJCCCGEEDZSqhq2zzzzDM8880xJxxBCCCGEECWgdLy0TAghhBBCPPKkYSuEEEIIIUoFadgKIYQQQohSQRq2QgghhBCiVJCGrRBCCCGEKBWkYSuEEEIIIUoFadgKIYQQQohSQRq2QgghhBCiVJCGrRBCCCGEKBUURqPRWNIhhBBCCCFEycnIyLDKcl1dXa2y3AeRHlshhBBCCFEqqEs6wH9N+29/LukIxbb23a4ANJ8yv4STFN/eIb1IyNWVdIxiC3Qw7UKtv/6xhJMU3/YBPQBoMH5OCScpviPD38G+z+iSjlFsebM/BWBZk//OIbbzQdN+9+Ha7SWcpPgmtW9N9bHTSzpGsV0Y+z4AjSZ+V8JJiu/w0LcJHjmlpGP8JXHjhnB8/FslHaPY6g6fW9IRShXpsRVCCCGEEKWCNGyFEEIIIUSpIA1bIYQQQghRKkjDVgghhBBClArSsBVCCCGEEKWCNGyFEEIIIUSpIA1bIYQQQghRKkjDVgghhBBClArSsBVCCCGEEKWCNGyFEEIIIUSpIA1bIYQQQghRKkjDVgghhBBClArSsBVCCCGEEKWCuqQDlHYK4N3m9alYzoN8g54Zu37jWnqmRZ0yDvZMfOlJ3l++gXy9AYDvu7cnKS0DgMhrt1j02ymbZh7cpimVvMqRr9czcctert7JsKjj7ujArK7P88aPq9Hq9ebpzSsF8XiVYD7dsMtmeQEMBgPTx31GbFQkGjs7Bo/5BL/AoCJ1Pu7XhyatnqBdp84YjUa6PvkEfkGmemG1Iug1YJBN8iqAAU80JtTTA63ewJRtB8z/v+9xc7Rneqfn6L14Lfl6Aw5qNSOeaU4ZB3ty8nVM2LKXtJw8m+S9X/6hTzensnc58nV6Pt+4m8Tb6RZ13B0dmP/6i3Sdt8JiG7FKHoWCb7o8T7i/D3k6HX0WryXmRqq5vGezevRuXh+dwcCEDbvZcDaKcs5OLOzZEQc7Dcl30nlr4Rpy8vN5ukZlRrZtBcDJhGTeX7oeDydHfnjzZVwd7EnNyqbPknXcyMiy6nd6kLJhDYnoO56dfVuXyN//PQXwUq2q+Lq5ojMYWHHyAreycszlzUMCqO3vDcDFlFtsjYxDo1LSrV5NHO005Ov0/Hz8HFnafOvmVMDoto9TzdsTrV7PqHXbSUhNM5e/UrcGnerXRG8w8u2e39gVdZnhzzSnmo8XAJ4uTmTkaukybzkjnm1B3QBfsrRaAPr+vJ7MPK31sgMfPfUYlcuXQ6vX88XGPSTeKbq/zXutPa9+/4vF/tayckVaVwth9P92WC2fOacCPnuhDdV9vNDq9AxbvYX41Dvm8i71w+nasBZ6vYEZuw6zIzIWR42az9u3IcDDDY1Kxdj1OziVeA0AB42aRW92ZOiqLcTeTH3Qn7XGNyHg6W44evtj1OlI2PgjebdvmEvLN3yKsmENMWLk2oENpEWdsGE28Wf+sMdWr9fTq1cvWrRowerVq4u1wMjISI4cOQJA9+7d6dixI927d6dbt260a9eO3bt3AzBu3DiSkpJIT0+nc+fO9OzZk6SkJHbsMO18w4YNY8+ePRbLbtas2V/+goUlJibSqVOnItMnT57MqlWr/tGyH6RRsD8atYqha7ay8NApejapY1Fex9+HT55/HHdHB/M0nzIuxNy4zcfrdvDxuh02bdSCqXFqr1LR5+f/8e3eI/Rt2ciivGGQH1M6PkNZJ0eL6e8/3ph3mtdHqVDYMi4A+3dsR6vNY/qin+g1YBBzpkwqUmfBjOlkpBWcyJKuJFC5enWmzP+BKfN/sFmjFqBZaCB2KhX9l29k3v5jvNu8vkV5/UBfJr74JB6FtovnalYm+votBv6yiV1RcbzWoJbN8v5eqyrB2KtV9Fq4hhm7DjPwiSYW5Y2D/ZnRpS1lnR0fsISH64WIathr1LScNJeP12xl4stPm8u8y7jQ9/HGtJo8j+enL+SzF5/ETq1iZNtWLD1ymtZT5nPyyjXeal4fF3s7xnd4ipdmLqHFl3OJv3UHTxcnhj7Tgv0xCTwxZT6zdh3m0/ZtbPK9fq9atw9oMGIOKjuHP69sIzUqeKFRKZmx9ygbzl+iXY3K5rKyTg7UCfBhxp6jzNhzlCpeZalQxoVGQX4k3slg9r5jnLyaQusqwVbP2aZaKPZqFV3nr+CrbQf46Knm5jJPFydeaxTBq/N/ofeiNQxq3RSNSsX4TXvp8cMqei1cQ0aullHrtgMQVqE8vRevoccPq+jxwyqrNmoBWlapiJ1aRe/Fa5m1+zcGPNHYorxRsD/TOz9XZH8b3LoJ77VsaLNj8lPVK2GvVvHynJ+ZuGUvI59raS7zdHGiR5M6vDJnKT1+WMmHTz2GnUrF280bEJVyi05zlzF89RZCPD0ACPfzZtlbnQkq626T7IW5V6mNUq0hauEEru5ahd8TBe0Glb0j5es/QeTC8VxaOhX/Np1tnk/8sT9s2N64cYPbt2+zZ88eXnrppWItcMuWLVy6dMn8eeLEiSxatIglS5bw9ddfM2mSqcExcuRIfH19iYqKonz58nz//fccOnSI48eP/4Ov8+8T5uPFiYRkAKKu36JS+bIW5QZg9P92kJFX0PNWyass5Zwd+bzdE4x6riV+bq62jEwtPx8OX74KwPnkG1Tz9rQoN2Bk0IqNpOda9haeTUphyrYDNstZ2LkTx2nQ9DHA1PMade6cRfmerZtRKhU0eKzgZBZ9/jw3r1/ng15vMKLvu1y5HGezvOG+5TkSb1rHF67dpOrv1rERIx+t3kpGoRPmqpMXWHLkDADlXZ25nZNrs7y/FxHgw4HYKwCcTbpO9QpeFuUGo5G+S9eTbqMe5WahQWw5Hw3Ab3GJ1A3yM5c1qOjHwZgEtDo96bl5xNy4RbifN01DA9ly3nSs2nwuiieqhdIkJJCzV1OY2PFptg/pRUpGJjczs6lewYvNZ6MAOBCTQLPQQJt8r9/LvBrD/mGvlMjffpDgsu5cvG7qTUu4nY6/e8Hx6k5OHvMOnsQIGAGlUkG+Xs++2CtsjzLtb+6ODmTmWX87qRvoy75L8QCcSrxGTd/y5rJwP2+OX0kmX68nM09LQmoaVb3Lmcu7NYrgQEwC0ddvoVBAUFl3Pm33BEt6dqRDnTCrZ4/w9+FQXCJg2t/u9SLfYzQa6bf0V9J+d0w+fTWFL7fstXq+e+oH+bE76jIAJ68kE+7nbS6r7V+BYwlJaPV6MvK0xKfeoZqPJy0qV0Sr1/PjGy/T//HG7Ik2zW+nUvHuknUWd15sxTmgMumxZwHITorFqULB3T99vhZteipKO3uUGnswGm2eT/yxPxyKMGrUKC5fvszo0aOpXr06ISEhTJ48GY1GQ6dOnYiLi+PQoUMYDAbatm3Ls88+y+rVq9FoNNSoUaPI8pKSkihTpgxg6s0dOXIkn332GdevX2fatGls2rSJ3Nxc6tSpU2TewpKTkxk1ahR5eXnY29vz2Wefodfr6dOnD+7u7rRo0YKIiAhmzJgBQG5uLhMnTkSj0ZiXsXnzZmbPnk3ZsmXJz88nJCTkL6+84nCy01jcYjMYjCgVCgx3d4Z7t1wKS83O4ZcT5zkQe4XqPp4Mat2ED1ZtsUq++3G211j0QBiMRlQKBfq7mY/GJ913vh2RcdT297FJxt/LysrC2bXghKpUKdHrdKjUauKio9mxYQOjp0xl8ZzZ5jplvbzo0ustWj71NGePH2PCiKHM/Gm5TfI62WnIyivYLvRGg8V2cezuxdDvGYxGJnd4iuByHny02nbbxO8522nIKryNGAwW28hvdy+MbMXV0d6iEa03GFAplegNBlwd7EkrdBGQkavFzdGBMoWmZ+ZpKeNoTzkXJ1pWCabhF7PJzNOyY0gvDsde4VTiNZ6PqGb6b61qONppimSwhcRdq3HyCfrzijZkr1GRm68zfzYYMW/LBqOR7LvHv+drVCIpLZObd4cpGIF3mtbBp4wLcw9Y/1aui70dGbkF26zeaESlVKA3GHGxtyOzUKMwS6vF1cEeAI1KSed6Nek0dxkAjhoNS347xQ8HT6BUKPjxjQ6cTUohKuWW1bI729n94TH5Qfvbtoux1A2oYLVcv+fqYG/RSaM3WK7jjMLrOM+0jj2cHHFzdKDHDyvpUDuMEc+2ZMgvmziWcP/zjC2o7BzQ5xUMp8FgAIUSjKahgtr0VMLe+gQUSlIObiyhlA9fpoOLVZZr2665P2nYjhkzhsGDB+PlVXB1mJeXx4oVKwBo2bIlixcvxtvbm1WrVuHt7c1LL72Ep6cntWqZbpMOHToUtVpNUlIStWvXZvz48eZlaTQaRowYwdKlSxk4cCCBgYHExsbSunVrtm7dyqRJk5g7d665ftrd28gTJ06ke/futGzZkoMHDzJ58mQGDRrEjRs3WLlyJXZ2dixZsoRJkybh7e3Nt99+y6ZNm2jXrp15WZMmTWLFihW4u7vz9ttvP4RVeX/Z2nwc7QpWs6JQ4+VBLt1IxWAw1blw7SblbHQ7956svHycCp24FYUOoP9Wzs7O5GQVjHk0Goyo1Kb1vm39Wm5dT+HDt3qSknQVtVqDj58v4XXro1KrAKhZtx43r1/HaDSisMFtu99vF0r+fLu454NVWwjwKMMXL7Sh+4/WGULzZ7K0/65tJCMnDxd7O/NnpUKB3mA6CWXk5pkbKQCuDnbcyc4l/e703HwdLvZ2pOXkkpqVzbH4JFLujoPfF32ZWv4+fLlpD191eo6NA3qw5dylIuOJH2V5+Xrs7+5HYBpnWXhbViuVdKpTnTydnlWnLlrMO+fACbxcnOjVOIIJ2w5aNWdmnhbnItuI8b5lznZ25jtSTUICOBp/1dywzM3XsfDQSXNj/nBcItW8vazasM3Sai32N6WCf+UxOSM3Dxe7Yq5je9M6vpOdw7YLpjsn2y7G8G7LhrYNfR96bS7KwsN9CjVq3UJqonFx4+ys4QBU6jKQzMRLZCdftn3Qh8xotW3KtsMT//JbEYKDC8ZCffXVV3z11Vf06tWL9PT7H+gnTpzI0qVLee+990hNTaVCheJfPX744YcsWrTI/OPm5gZAVFQUc+bMoXv37sycOZPUVNOtCn9/f+zu7lTe3t6MGzeOYcOGcfjwYXS6gh6Fmzdv4uLigoeHBwqF4k97iP+JC9duUC/QF4Aq5ctZDKR/kC71atKuVlUAKpZz50ZmttXy3c+ZpBSaBPsDEFbBy8aD9v+eGnXqcHifaUz2+dOnCK5cMM7vrUEf8M2SpUyZ/wNPvfAiL3fvQYNmzVn07SxWLV4EQEzkRcr7VLBJoxbgbPJ1GlU0rePqPp7E3br9p/N0rV+TNtVMdxZy83UY7h5oS8KpxGvm2/E1fcuXyO3Cwg7EJvBMzSoANAz251zSdXPZkctXaVYpCHu1mjIO9lTz8eJc0nUOxiTwzN3xoE/XqMK+S/EcT0iihm95yjk7oVIqaRgcwIVrN2heuSJLDp/i2a9/JO7WbQ7GJJTI9/w3upx6h+p3b9sHepQp8nDsG41qkZSWycpTF7l32ny8chB1797d0er0GGzQRjuekESLyqbe7gh/H6JSbprLzlxNoV6gL3ZqFS72doR4eRB93dRQbRISyN67QxjAdExe0rMjSoUCtVJJ3UBfzidfx5pOJ6bQNCQAMO1vl0p4f3uQYwlJtKpqaiPUDqhAZKF1fDIxmQZBftipVbja21HJqxyRKTc5Gp/E41VNx7VGwf5EF5qnpGQlXsItNBwAJ98Qcm4kmst0udkYdPkY9TqMeh363BxUDk4lFVXcx19+K4JSaWoLa7VaNm3axFdffYXRaKRt27a0bdvW1CNpKHrC7dKlC8eOHWPq1KkMHTr0gcu+37y/FxISQs+ePalbty4xMTHmh9XuZQP4+OOP2bZtGy4uLgwdOtTiSsTd3Z2MjAxSU1MpW7YsZ86cwcfHOrfQD8UlUtvfh4kvtgEUTN91iBdqVeVaWia/xd//9tHKE+cZ3LoJ9QN90RsMfL3zkFWyPcie6MvUD/JlVtfnUaBg/OY9dK5Xk8Q76ez/l57Qmz3RhmMHDzLg9W4YjUY++PRzfln4A76BgTRt9cR95+nSqzcTRgzj8N49qFQqPvxsnM3y7ruUQL1AX6a/8iwKBXy5dT8d64Rx9U4GB+Ou3HeeTecv8dGTj/FsjcqoFAq+3LrfZnl/b1dkHI0q+jO/e3tQKPh0/S5ebRBO4u109hRqBNjK2pMXaFMtlF0f9EahUPD2wtUMaN2UmBu3WH86kpk7D7FjSC+USgWj120nT6dj/MbdzO/RgZ6P1edWZhavf/8L2dp8Pl67lfXvvw7AL8fOcj7pOnn5Oub36ABA0p103lm81ubf8d/qbPINKnuVpW/zeihQsOzEeVqEBnAzKwelQkFIOXfUSiXV7jZ+N56/xJGEZLrUCaNhkC8KhYLlJ85bPee2izE0DQ3kp16voABGrN1GjyZ1SEi9w87IOBYfPsXiN00N1mnbD6LVmd4sEFzOnbWnLpiXE3vzNuvPRLK0dyd0BgNrT12wekNzV1QcDSv6Mfe1F1Cg4LMNu+h6d3/bWwL724NsPh/NY5WC+OXtrigU8OHKzfRqVo/4W3fYdjGGHw+eYPlbXVAqFEzeug+tTs/M3YeZ8NJTrHynKzq9gcG/lPyt/TuRJ3CtGEaV7kNBoSB+/Q+Ub/Akebevk3bpFNnJ1anaYzhGo5GsK5fIiLP+9msLxb1r+G/3t1/3ZWdnh5ubG+3bt8fNzY1mzZrh6+tLzZo1+fLLLwkNDS0yz8iRI3nhhRdo3779fZdZpUoVZs+efd/xuYUNHTqUsWPHkpeXR25uLiNHjixSp3379nTq1IkyZcrg6enJ9esFV9RqtZrx48fTq1cv3NzcUKut99YzIzB771GLaVfvRBap9/aS/5l/z9Lm89nGPUXq2IoRijwEVvi1OPd0mld0POrJxGucvM+4YWtTKpUMHDXGYlpgcNFx06/36Wv+3bWMG+NmzC5SxxaMwLQdlhcsV24XPTh2W7DS/Pvt7FyGr91m7WjFYgQmbLZ8KOV+dyPaz/7JNnmMRvr9/D+LaYV7i77ff4zv9x+zKL+ekUW7GYuKLGvF0bOsOHrWYlrMjVRaTZ73EBP/fdnX4tn21j97Q8zDZARWnbY8phW+yzRi/a77zjfv0EkrpirKaIRP1u+0mBZ3s+BOyYrj51hx/NzvZ+Pdn/5XZNr8/ceZv992DzobgYlb9llMi089U6TeS9/+XGTa8SvJHL9y/zH7D5vRCB//7hhV+I7f0qNnWHrUMndaTi59flr3wGV2nW+b5x4sGbmyebHFlOupBee15L3rSN774MyiZP1hi87f35/lyy03qkaNCl791K9fP/r162dR3qpVK1q1agVA48aWryRxd3c3v8Jr0SLTCSU0NNS8zLCwMDZv3gxA27Zti+TZv9/UQxUQEMD8+fOLlBfOOnz4cIYPH/7AOg0bNiz2K8yEEEIIIUqz0tJjK//ymBBCCCGEKBXkXx4TQgghhHjEGWzxFKcNSMNWCCGEEOIRJ0MRhBBCCCGE+BeRHlshhBBCiEec9NgKIYQQQgjxLyI9tkIIIYQQjzh5eEwIIYQQQpQKMhRBCCGEEEKIfxHpsRVCCCGEeMQZjIaSjvBQSI+tEEIIIYQoFaTHVgghhBDiEWcsJWNsFcbS8k2EEEIIIcTfcvpWhlWWW6ucq1WW+yDSY/sXXf7lm5KOUGwVO/YH4LmZS0o4SfFt6NuNzTFXSzpGsT0d6gfAmelDSjhJ8YW/PwUA3bXYEk5SfGqfEAau3FLSMYpt2stPAfDh2u0lnKT4JrVvDcCyJv+d00LngzrOzRxa0jGKrUbfiQDkxZ8v4STFZx8URtrJ3SUd4y9xq93yP3keEQ/Hf+cIJoQQQgghrEJe9yWEEEIIIcS/iPTYCiGEEEI84qTHVgghhBBCiH/AYDAwevRoOnfuTPfu3YmPj79vnd69e/Pzzz//6fKkYSuEEEII8YgzGo1W+fkz27ZtQ6vVsmzZMoYMGcKECROK1Jk2bRppaWnF+h4yFEEIIYQQ4hFXUkMRjh07RvPmzQGoXbs2Z8+etSjftGkTCoWCFi1aFGt50mMrhBBCCCGsYtmyZXTo0MH8s2zZMovyzMxMXFxczJ9VKhU6nQ6AqKgo1q9fz4ABA4r996THVgghhBDiEac3GKyy3M6dO9O5c+cHlru4uJCVlWX+bDAYUKtNzdM1a9aQkpJCjx49uHr1KhqNBj8/vz/svZWGrRBCCCGEKBF169Zl586dPPfcc5w8eZIqVaqYyz766CPz79988w2enp5/OiRBGrZCCCGEEI+4khpj++STT7J//366dOmC0Wjkiy++YMGCBQQGBtK6deu/vDxp2AohhBBCPOKK8wYDa1AqlXz66acW00JDQ4vU69+/f/GW91BSCSGEEEIIUcKkx1YIIYQQ4hFnMJSOf3lMGrZWZjAY+WbdLuKu3USjVjHwpSfwK+cOQEzSDb7dsNdc98KVFMZ0e44GVYIAWLX/JLczs+n1dFObZlYAfVs2JNjTnXy9ga93HiI5LdOiThkHe6a8/BTvLf2VfH3Bk5T+7mWY2vFpXl2w0mK6tRkMBlbM/JqrcTGoNRq6DvgAL18/c/me/63ht22bQQHPdH2dmo2asHX5T1w4dgSAnKxM0m+nMm7JShslVuD7eAccPX0x6HVc3b4cbdotABw8fanQor25ppNPEPG/LsA1sBoOXr4AqJ3KYNDmELN8uo3ymtbxZ1NnEnkpFjs7DZ98OJAgf19z+Rdfz+bE2fM4OzkC8M24MahUKj77agaJydfI1+kYMaAPtapXtUleBdCxTnX83FzRGQwsPXaOm1k55vKWlQKpG1ABgPPXbrD5QiwOajWvNQzHQa1CrVSy5nQkl1OL91Lwh5X5pVpV8b2becXJC9wqlLl5SAC1/b0BuJhyi62RcWhUSrrVq4mjnYZ8nZ6fj58jS5tvs8x/pmxYQyL6jmdn378+Vs56FFRo+SIOnhUw6nUk7VxZaP+rgM9j7cw1Hb0DubJxIS6BVXHwNG0vaidX9Hm5xK2caZO0BoOBcd/MITL2MnYaDWMH9SXQr4K5fMLMeZw4d8G87339yXBmLVxKZEwcADdT7+Dq4syS6RNtkvde5onzfyI6PhE7jZqR77xOgE95c/mBE2eY98t6AKoGB/JRr1fJyslh9Dffk5WTQ75Oz8DXX6FWlaK3pK2V96+eQwx6PavnziYhOhJdfj7PdutBzUZNbJJXPJhNGrZ5eXmsW7eOV155xSrLHzZsGM899xwtWrRAp9MxZMgQPDw8eO+995g1axZjx47liSeeYOPGjYwZM8Zc1xYOXIglX6dn2ruvcCHhGt9t2M8n3dsCEOrrxaTeHQDYc+YSZV1jaFAliLx8HdNW7+BiYgqP1bDNTl1Yk5AANColQ1Zuoap3OXo3q8tnG/aYy+sGVODNJrXxuHsQvcdRo6Z3s7o2bdDec+bgPvLztQz+agZxF8+zet5s3h79OQCZaWns+3UtQ2fMJV+r5Yt336RGw8Y82elVnuz0KgBzxozghTfftlneMqE1Uao0xKz4BkefQCo0f4H49QsAyL2ZRNyq2aZ6lWqhy0onMz6SzPhI08xKJaEd+5G4fbnN8gJs33eQPK2Wn2ZP5dS5C0yaNZcZX4wxl5+PvsR3kz7Hw93NPG3mgsVUCg5i/MgPiIyJI/JSrM0atuG+5dEolUzb9RtBZd1oX6sq8w+eBKCcsyP1AiswdcdhAPq3bMDpq9eJ8PMm+votdl9KoLyLE90b1mLKjkM2yQtQo4IXGpWSGXuPEuhRhnY1KvPDb6cBKOvkQJ0AH77ZbboYe++xepxNvkGopweJdzLYFhVH/YAKtK4SzLqzUTbL/EeqdfuAoGe7oc/JLukoFlxDwlCo1MStnIWjdyDezdpyZcNCAHJvJnN5zXcAlAkNxzUrncyEKDIT7q5TpZLgDn1I2mmri2DYceAwedp8Fn89kVMXIpn83QKmfzLCXH7hUgzfjh+Dh1sZ87ShfXoBkK/T0WPQCMYMes9meQF2HzmJNj+f7z8fxpmoWL5etILJH/YFICsnl+mLV/LtmCG4l3Fl4dpN3MnIZMWmnTSoWY2ubdsQn3SNj7+ey6KJo2yS9++cQ47s2Iper2PQlG+4c/MGJ/bttklWaymph8ceNpuMsb1x4wYrVqyw+t/Jz89n4MCBBAQEMHbsWMqXL8/YsWOt/nf/yLn4JOpXCQSgeqAP0VevF6mTq81n0fbDvPe8qbGt1eloU6caXVvVt2nWe2pU8OJYQjIAkSm3qOxVzqLcaDQyYu12MnLzLKa//3gjfjx0kry7L1a2pZhzZ6lerwEAwdXCuBIdaS5zcXNj6Mx5qNRq0m+n4ujsgkKhMJef2r8HRxcX8/y24OwbTEb8RQByriXgWD6gSB2F2g7vxk+TtHuNxXTP9IqKMQAAIABJREFUiMfITIgi79Y1m2S95/jpczzWsB4AETWqcy4y2lxmMBhISExi7OTpdOs7hFW/bgZg/2/H0GjUvPXBSL798Sea3Z3fFkI83bmQYuqFi09NI8Cj4KR/OzuXOfuOYwSMgEqpRGcwsPtSPAdiEwFQKhTorPRexwcJLuvOxeupACTcTsff3dVcdicnj3kHT5ozK5UK8vV69sVeYXuUqWfO3dGBzLy8+yy5ZGRejWH/MOt0aPwTThWCzQ3VnJQEHL38i9RRqDV4NXySa3vXWUwvF96MrIRo8lJtt/+dOHuBZvXrABBRvSrno2LMZQaDgfiryXw6bRavDxzO6k3bLOb9ec2vNK1XmyrBQTbLC3Ay8hJNImoAEF4lhAsx8eay05ExVAr0Y9qiX3hrzJeUdS+DRxlXurZtw0tPms6DOr0BOzuNzfL+nXPIheNHcPf04tsxw1k6fcp/vrfWYDRa5cfWbNJj++2333Lp0iVmzJjBmTNnyMzMRK/XM2DAALKysjhw4ACjR49mzpw5nDx5ktmzZ7N27VqSk5Np3749o0aNIi8vD3t7ez777DP0ej19+vTB3d3d3POq1Wrp378/NWvWpF+/fgAkJiYyePBgli8v2rMVFxfH8OHDUavVqFQqvvzyS7y9vR/6d8/OzcfZ3t78WalUoNcbUKkKrik2HT1P85qVcHM29YC6OjpQr3IgW45feOh5isPJTkN2oVuZBqMRpUJh3kBPJBY9oHdrEM6Ry0nE3bpjs5yF5WZn4+jkbP6sVKrQ6/WoVCrA9C+Z7PnfajYs/pGWL7xkMe/W5T/TY+jHNs2rtHNAr801fzYaDaBQgrGgIVW2RkPSok+jzy14cbVCqaJszSZcWva1TfMCZGVn4+pceB0r0en0qNUqcnJzebXDC/To9BIGg4E3Bw6jRrUq3E5LJz0jk7mTx7F20zYmz5rH+JEf2CSvvVpNbn7BRZax0HZsMBrNt+tfCK/C1Tvp3Mgs6FV0tbfjtYbhrD4VWWS5Vs2sUVlkNhixyHxvv3y+RiWS0jLNQyuMwDtN6+BTxoW5B07YNPMfSdy1Gicf2zaoikNlZ4/BYv8zFtn/PMIakB5zBn1uwXahUKrwqNGI2F9m2DRvZnYOLs5O5s9KpRKdXo9apSInN49X27el+8svYDAY6PXhKGpUqUSVkIrk5+ezYsMWfvrmS5vmBcjKzsWl0F09pVJhzpyWkcnRc5Es/nIUTg72vD1mEuGVQwnyNZ2Db95JY8yM+Qzu0clmef/OOSQrLZ0bVxN5Z+wXXDp7mp+++pIBk2x/bBaWbNJj++6771KpUiWysrJo2rQpS5Ys4euvv2bkyJE0bdqUI0dMt9aOHj3KtWvX0Ol07Ny5kyeffJKJEyfSvXt3Fi1aRK9evZg8eTJg6gWeP38+b731FgDjxo0jOzublJSUYmU6cOAANWrUYMGCBbz77rukpVlnHJ2Tg4Zsrdb82Wg0WjRqAXaciuKZ+mFW+ft/R7Y2H0e7gmuewo3aB3m8ajBPhYUy4cU2eDg5Mu4F246nc3ByIjenYCyiwWAwH5DuadHuJT5fvIKYs6eJOmU6+ScnXMbR2cViLJUtGLS5qOwKLngUCoXFSRXAvWpdbp87bDHNJaAyWVdjLU7KtuLs5ERWdsE6NhoNqNWmdexgb0/3ju1xdHDA2cmJhnUiiLwUi7ubK483awxAq2aNOBtpu1vkeTod9uqCbUCB5XasVirp3sA0nnbFiYKLyAplXHivRX1+PXuJmJu3bZYXIC9fb5lZQZHMr9argb1azapTFy3mnXPgBLP2HeP1huE2y/tfpdfmofyT/c+tSh1un//NYppzQCWyk+Jsvv+5ODmSnVPwNw1GI2rVvX3Pjm4vPY+jgz3OTo40rB1OZOxlAA6dOE298DCLC1JbcXZyIKvQXT1jocxurs6EhVbE090NJwcH6lSvTFT8FQAuJSTS97OveK/Li9QNs82wJfh75xDnMmWo0bAJCoWCyuERXE9KtFleazAYjFb5sTWbvu4rJiaGBg1MXf3e3t64uLiQnZ1NcHAwp0+fRq1WU7t2bY4cOUJycjKhoaFERUUxZ84cunfvzsyZM0lNNd2m8/f3x87Ozrzs1157je+//56oqCjWrl37p1k6duyIh4cHvXv3ZsmSJUU24IclLLACRyJNt2AuJFyjorflbf2s3DzydXrKF7rlWNLOJ9+gfqDpoaCq3uW4XIxe2N6L1zFszTaGrdnG7ewcRq7bbu2YFkLCanL+qKkRGHfxPL4VQ8xlKYkJzPt8tOmiQq1GrdGgUJo2/agTx6lev6FNswJkJcXhGlQdAEefQHJvJluUK+0cUKjU5GdarnuXwCrmIQy2Vic8jD2HTRehp85doHJwsLns8pWrvNbvA/R6Pfk6HSfOnCOsSiXqhtdgzyHTPMdOnaVSRdv13sXevEOYjycAQWXdSE63fACyd9PaJKVlsPzEBe4der1dnXmjcQSLfjvNhZSbNst6z+XUO1S/e4wI9CjDtd9lfqNRLZLSMll56qI58+OVg6jr7wOAVqenlDzYbFXZyZdxCTQ1mhy9A8n93bCee/ufLtOyw8PZvzIZCbbf/2rXqM7e344BcOpCJJUrBprL4q8m0WPwiIJ979wFqlcyHf8OHT/FYw3q2jwvQETVUA6cOAPAmahYQgMLOg+qhQQRc+Uqd9Iz0On1nI2OJcSvArGJSQyfOofP+vemaR3bXqD9nXNISI2Cea7GxuDhVf6+yxa2ZZOhCEqlEoPBQGhoKEePHiUsLIyUlBTS09Nxd3enTZs2TJo0idatWxMQEMDUqVNp2tT0JoCQkBB69uxJ3bp1iYmJMffuKpWWbfLKlSujVquZPHkyXbt2pWbNmtgXGgLwe9u3b6devXr069eP9evXM2/ePMaPH//Qv3uzsFCOX7rCwDm/gNHI4JfbsHLfCXzLudOkejCJN+/g7fHvadQCHIi9Qp2ACkzu8BQKBUzdfoiXIqqRlJbB4ctXSzrefdVq+hiRJ47x1ZB+YIRugz5ix6oVePn6Et64GX7BoXw1uB8KhYLq9RtSOTwCgJTEK1SrY7txn/ekx5zFJbAKIa/0RwEkbluGZ50W5N25RUbcOezdvdCmF+0ttHP34vaFozbPC9CmeVMOHj1Bt/cGYzQa+XzYYH5YtopAf1+eaNaY59s8Ttc+g9Co1bzwdGsqBQfx1mtdGPPlNF7tMwi1Ws34EbYZhgBwJuk6Vb3LMaBVQxTAT8fO0qpyEDcys1EqFIR6eqBWKql+t/G7/mw0rasGo1Eq6RBRDYCcfJ35gTNbOJt8g8peZenbvB4KFCw7cZ4WoQHczMpBqVAQUs4dtVJJtbuN343nL3EkIZkudcJoGOSLQqFg+YnzNsv7X5URew6XgMoEd3gPFHB1+wrKRTRHm3aTjMsXsHP3JP8++5+9uydpkcdsnrd1s0YcOn6S7gOHYTQa+WxIfxb+spYAvwo83qQhzz3RgtcGDEOtVtGuTSsq3W34Xk5Mot2Tj9s8L0CrBnU4fPoCvUZNwGiE0X16sGT9VgJ8vGhRvzZ9u77E+1+Ybtu3blKf0EA/Ppg0E22+jq9+XAaYeqrvPXBmbX/nHFKxWnWWz5jGlEF9wWikc79BNslqLaXl4TGF0Qb/1EReXh6dOnUiPDyc1NRU0tLSyM3NZcCAAbRo0YKMjAyaNGnCmjVr8PHxoXHjxixfvpywsDCuXLnC2LFjycvLIzc3l5EjR+Ll5WUxdrbwWxEA1q5dy9y5c5k2bRojRoxg+fLlRd6KULFiRT788ENUKhVKpZLhw4dTo0aNP/0ul3/5xqrr6mGq2NH0r3Q8N3NJCScpvg19u7E55t/ZeL6fp0NNvRBnpg8p4STFF/7+FAB012JLOEnxqX1CGLhyS0nHKLZpLz8FwIdrbXvn4p+Y1N40fGhZk//OWyA7H9RxbubQko5RbDX6ml63lRf/37n4sA8KI+3kf+tpf7faLf+T55GSZq11ZuvvZ5MjmL29/R8OD3B1deXs2bPmz4V/DwgIYP78+UXmKfxA2IQJEyzK2rdvT/v27S3q7dixo0jdZcuW/ZWvIYQQQggh/sX+O5fmQgghhBDCKkrLUASbPjwmhBBCCCGEtUiPrRBCCCHEI84Gj1zZhPTYCiGEEEKIUkF6bIUQQgghHnF6G/8T4tYiDVshhBBCiEecPDwmhBBCCCHEv4j02AohhBBCPOKkx1YIIYQQQoh/EemxFUIIIYR4xJWW131Jw1YIIYQQ4hEnQxGEEEIIIYT4F1EYS0vfsxBCCCGE+FuWnYm2ynI7h1e2ynIfRIYi/EU5l06WdIRic6xUG4AG4+eUcJLiOzL8HT7ZvK+kYxTbmKcfA+DUlP4lnKT4IoZ8A0BUem4JJym+KmUcqPTx1JKOUWyXPh8EQPWx00s4SfFdGPs+AOdmDi3hJMVXo+9EljX575zGOh/UAZCRkVHCSYrP1dWVIym3SzrGX9LA24NXf1hV0jGK7ac3OpR0hFLlv3NEEEIIIYQQVlFaxthKw1YIIYQQ4hFnNJSOhq08PCaEEEIIIUoF6bEVQgghhHjElZahCNJjK4QQQgghSgXpsRVCCCGEeMRJj60QQgghhBD/ItJjK4QQQgjxiCstPbbSsBVCCCGEeMQZ5HVfQgghhBBC/HtIj60QQgghxCOutAxFkB5bIYQQQghRKkiPrRBCCCHEI6609NhKw9bKDAYDX8yaT1RcPBqNhjHvv0Ogr4+5fOK3Czh5PhInJ0cApo36kPSMTEZ9NRMjUMHLk1H938bRwd6qORXA0KebU9m7HPk6PZ9v3E3i7XRz+YsR1XipThh6g4HvDxxn36UEfN1cGfP84ygUcC0tk3Eb95Cn0/Faw1o8FVYJI0YWHDjBrqjLVs0OYDQYOLJiMbevXkGl1tCoaw9cvbzN5Rd3biH++G8A+IaFE/5se4xGI2tGf2Cu51kxlNovvGz1rCYK/Np0wtHLD6Nex5UtP6G9cxMABy8//B4vyOFUoSKX184lL/U6Ac++BijIT0/lytafMerybZTXtC3PnjiOuOgoNBo7+n88Bt+AwCJ1PhnYj8YtW/Hsy53Izclm8sfDyUhPw8HBkcGfjsPNo6xVcyoU8Em71lT38USr1zNi9VbiU9PM5Z3r16RLg1roDQZm7jrMzsg4HDVqPn2hNf4ebmhUSj5dv5PTV1MI9/NmxLMtUSjgZkY2g3/ZiFanf+h5R7d9nGrepryj1m0noVDeV+rWoFP9mugNRr7d8xu7oi4z/JnmVPPxAsDTxYmMXC1d5i1nxLMtqBvgS5ZWC0Dfn9eTmad9qHnv8w2o0PJFHDwrYNTrSNq5Em3aLQAcPCvg81g7c01H70CubFyIS2BVHDwrAKB2ckWfl0vcyplWzll8ZcMaEtF3PDv7ti7pKIBpv5owYQLR0dFoNBpGjRpFQEBAkToDBw6kRYsWdOzYkbS0NEaNGkVWVhZubm58/PHHlC1r3X3vQdl/+GoSCTHRqDUaen80Ah//otknDx1Mvcda0Lp9B5tnBNM58M0mtQnycCPfYGDu/uOkZGRZ1HG1t+OTtq0YunYb+XoD7cKrEOFnOn842Wlwd3TgvWUbSiD9w1FaGrb/aCjCnj17WLZs2X3LvvnmG37++eci0/v16wdA9+7diYmJsSi7cOECM2bMAGDr1q2kpKQUO0uzZs3Mv8fExPD0009z8OBBVq1axfbt2zl8+DCDBg0qUtfadh48Qp42n4VTPmfAG135at4ii/ILMXHM+mwE8yeMYf6EMbg6OzH1+8V0fO5JFnz5CfVrhbF4zXqr52xVJRh7tYpeC9cwY9dhBj7RxFxWztmRzvVr0nvRGvov20Dflg3RqJS8/0RjVp04z9uL13EsIYluDcNxsbejc/2a9Fy4hn5Lf2Vwm6ZWzw6QeOYE+vx8nh48ktrtXub46uXmssybN7h89BBPDhrBU4NGkHzxPLevXiHz5nU8/INo8/5HtHn/Ixs2asGtUi2UKg2Xfv6K5L3r8G35krks98ZVYpZPJ2b5dG6e3ENa9CkyLl+gQsv23Dq1j5hl08hMjMar3hM2ywtwaNcOtHlaJn+/iB79BvD9tClF6iyePYPM9IJG2ebVqwitVp2Jc3+g+VPPsGz+XKvnfLJ6JezVKl75bhmTNu9j+LMtzWWeLk683rgOnb9bxhs/rOKDJx/DTqXireb1iUq5Rdd5yxm5ZhshXqYGwLgX2zBs1Wa6zF3OnujL+LmXeeh521QLxV6touv8FXy17QAfPdXcIu9rjSJ4df4v9F60hkGtm6JRqRi/aS89flhFr4VryMjVMmrddgDCKpSn9+I19PhhFT1+WGWDRi24hoShUKmJWzmLlIOb8G7W1lyWezOZy2u+4/Ka70g9c5D02LNkJkRxbd//TNPXzUOvzSVp50qr5yyuat0+oMGIOajsHEo6itmuXbvQarUsWLCA/v37M3Xq1CJ1Zs+eTVpawb63YMECateuzfz58+ncuTMzZ5bMhcOxvbvJ1+YxdvY8urzTl59mTi9SZ8W8OWSlp99nbtupH+iLRqVizIbdLD12lm4Nwi3Ka/mWZ/hTj1GmUCfT/85E8fmmvXy+aS+pWTl8u/eorWOL+/hHDdsWLVrQuXPnvzTPvYbr/VSvXt3c8F24cCGZmZl/OVN0dDR9+/ZlwoQJNGnShA4dOtC6dclddZ84H0mzehEA1KpWhXOXChrzBoOBhKRrfPbNd/T4YBRrtuwEIDYhkcfq1QagdvWqnDgXafWcEQE+HIi9AsDZpOtUr+BlLqtRoTynElPI1xvIytNy5XY6lcuXI9jTgwOxCQCcSrxG7YAK5OTrSE7PxNFOjaNGY7MrwOsx0VSoXhMAz+BQUq9cNpc5eXjQqs8glEolCqUSg16PSqMh9Uo8OWm32Tb9S3Z+O430lGs2yQrg7BdCxuXzAGQnX8bJO7BIHaXaDp+mz3F15y8AOJTzISPONE/W1Vic/UJslhfg/KkT1GtqulCpFl6L6AvnLMr3b9+KQqmkXtPHzNPav/oanXq+BcCNa8m4lytn9Zz1g3zZE30ZgJOJ16jpV9BzH+Hvw7GEJLR6PZl5WuJT71DVx5PmlYLI1+tZ0OMl+j3eiD3Rlwn29OBOdi5vNK3LT71ewc3Rgbibtx963rqBvuy7FA+Y9qOavuXNZeF+3hy/kkz+3bwJqWlU9S5Yh90aRXAgJoHo67dQKCCorDuftnuCJT070qFO2EPPej9OFYLJTIgCICclAUcv/yJ1FGoNXg2f5NredRbTy4U3IyshmrxU2+17fybzagz7h71S0jEsnDx5kiZNTJ0N4eHhXLhwwaJ827ZtKBQKmjYt6EiIjY01f46IiODkyZO2C1xI5JlT1Gpkyl6pRk3iIi9alP+2awdKhcJcp6RU9S7H6aumzrRLN24TUs7DotxghC+27DPfDSmsQaAvWdp8Tiddt0lWazEajVb5sbV/1LBdtWoVgwYNonPnzgwYMIAOHTowZswYc/n27dvp0aMH7du3Z8eOHYBlb+n06dN5/fXX6d27N6mpqeZe1V27dnHhwgWGDh2KVqvl+++/5+WXX6Zz585MmjQJMPUI9+zZky5duph7fi9evEi/fv2YNm0aderUMde7X88xwJIlS3jllVfo3LkzEydO/Cer4oGysrNxcXYyf1Yplej0pluZObl5dG33NOM+6M+sT0ew/NctRMXFUyWkIrsOHwNg9+Fj5OTlWSVbYc52GrIK9e4YDAZUCoWpzN7OoucnW5uPi70dUSk3aVG5IgAtKgfhoDGNbElJz2T5W51Y9ObLLDt61urZAXS5udg5Fqznew1YAKVKjYOLK0ajkeNrllHWP5Ay5X1wLONGjSefo837H1HjybYcWGT93sR7lPYO6PNyzZ+NRgMoLHfHsuFNuBN1En2O6XZYzvWrlAk19SKUCQ1HqbHu8JTfy87KwsnZ1fxZqVSh1+kAiL8Uze5NG+j2zntF5lOpVIzs05v1y5dSv1Cj11pc7O3IyP3dtqxUFCor2J+y8rS4Otjj4eSIm6MDb/64mu0XYxn+TAs8nBypG+jLksOneH3BSpqGBtAkJKDI33vYefVGo0XezMJ5taa8ABqVks71avL9geMAOGo0LPntFB+t2sJbi9fStUE4VbytfyGhsrPHoC28LRuLbMseYQ1IjzmDPjfbPE2hVOFRoxE3T+6xesa/InHXagw2HOJTHFlZWbi4uJg/K5VKdHf3vUuXLrF582beffddi3mqVq3Knj2mdbtnzx5yc3MpCTlZWTg5O5s/K5VK83HjSmwMB7Zu5uVeb5dItsIcNRqytQX/3w1GI8q750CAs8nXH3gH5IVaVVl18sJ9y4TtPZS3Ily+fJlx48axYsUK9uzZw40bNwDw9vbmxx9/ZMSIEfdtXD711FMsXLiQxx9/nDlz5pint2rViurVqzNx4kTi4uLYuHEjS5cuZenSpcTHx7Nzp6lnMyQkhKVLlxIaGkpWVhbDhg1DpVKRkZFRrNyrVq1i5MiRLFu2jICAAPOB4mFydnIiK6fggGIwGFGrVAA42Nvz6gvP4ehgj7OTIw0iahAVF8+Q3t3Zffgo7436AoVSgXsZ1wct/qHJ0ubjZKcxf1YoFOjvXmll5Wlxti8oc7LTkJGbx7Tth2hRKYjpnZ/DYIQ72bk0CwnA08WJ9rN+pt3MJbSqUpGwQr2/1qJ2cCC/0IHbaDCivLueAfT5+RxYOBddbh71O70GQNnAiviFmy6AyodWJufObZtdXRryclHaFWqYKhRgNFjUca9en9QzB8yfk3avpkxoOMEd+oDRiD7nr9/R+CecnJ3JyS4Yc2Y0GlCpTRczOzas59aN64zs8xbb169jzU+LOHZgv7nuuNnzmDB3AeOHDrF6zsw8LS72dubPSoUC/d0Xj/++zNnejvScPO7k5LLtoukCecfFWML9vLmTnUP8rTtcupGKzmBgT/Rlavp687Bl5mlx/oO8hcuc7exIv9vQbRISwNH4q+aTbW6+joWHTpKbryNbm8/huESqeVt/39Nr8yy2ZcV9tmW3KnW4ff43i2nOAZXIToqzaBSL+3N2diY7u+CiwGg0or677/36669cv36dd999l/Xr1/PTTz9x4MAB3njjDZKTk3nvvfdISUnB2/vhb7vF4ejsTE6h7IZCx419mzdw++YNvhjYl72bfmXjsp85dfhgieTMyc83d86AaTsuzh1HPzdXsrXaIuNx/4v0BoNVfmztoTRsAwMDcXFxQaVS4eXlRd7dHsYaNWoA4Onped+rxfr16wNQt25d4uLi7rvs2NhYIiIi0Gg0KBQK6tevT3R0NADBwcHmegqFgpkzZzJp0iSGDh3KrVu3/jT3+PHjWbp0Ka+99hpJSUlWadTUDqvKviMnADh9MYrKFQtuOcdfTeLNj0aj1xvI1+k4cS6SaqHBHDpxmnde7cisz0agVChoXDv8QYt/aE4lXqNZqClbTd/yxNxINZedS75ObX8f7FQqnO3tCPZ0J+bGbRoF+zF33zHeX7YBo9HIb5cTSc/NI0+nR6s3/WTkFvQwWZNXSCWSzp8G4GZcDO6+fuYyo9HI7rnf4OHrT8Mur6NUmjb7MxvXEblrKwC3r17ByaOc6aRsA1lJsZQJNu0fThUqknsz2aJcaeeAUqUmP+OOeZprUDVSDm4kbtVsMBrJiLf+EJXCqkfU4ej+fQBcPHOaoNDK5rI33x/ElB+WMH7OfFo//wIvvtqdek2bsWLBfHZs+B8ADg6OKFXWf8PgsfgkWlapCEBtfx8iU26ay04lXqN+kB92ahUu9naEepUl6vpNjsZfpVUV0/GkYUU/oq/f4srtNJzsNQSVdQOgfpBp+sN2PCGJFpWDANNQiahCec9cTaFeoK85b4iXhzlDk5BA9t4dwgBQsZw7S3p2RKlQoFYqqRvoy/lk698azU6+jEtgVcD0cFjuLcthBUo7BxQqNbrMNIvpzv6VyUiwvC0t7i8iIoL9+00XimfOnKFSpUrmsgEDBvDjjz/y3Xff8fzzz/Pqq6/StGlTTpw4Qdu2bZk1axa+vr5ERESUSPYqNWtx6pDpAv3SubMEhISay7r26c8nc77n4+mzaf5MW57t3JWIEhqSEHn9FrX9TQ92V/Ly4MrttD+Zw6Smr2moXmlgMBqt8mNrD+WtCA9qDPxZI+HMmTN4e3tz9OhRKleubFGmUCgwGo2EhISwYMECdDodKpWKI0eO8OKLL3Lx4kVzAwXAyckJPz8//Pz86NatGx988AHz58//w7+/fPlyPvnkE+zt7enVqxcnTpygYcOGxfzWxfNEkwYcOnGa14eMAox8MrAPi1avJ6CCD60a1+fZVo/RfchI1Co17Vq3oFJQADn/Z+++45q6/j+Ov5IQNk4EAREHuAEXKuK2Wqtdat1SZ62zVq1fR93aumtbt3Vrrdtaq1WrVkWsOHGLgBsQBypTAkl+f6ABioP2Z4LA5/l4+CjJuffmnduTm3PPPecm6SkTvl+EWm1G2ZKujOrf841mepGDIdepXaoEy/w/AoWCSb8fpLOPJ3cexXI47CYbTl7gJ/8PUaBgwaETaLRabsY8YWyrRqRotVy7/4jpe4+g1em4FHWfFd0+RqeHs7fvEnT9jtHzu3pV527IJfZ+9y169NTp0pPLB/ZgV8wRvU7HvbAQdKmpRF5OGxrh/UEbKjdrydHVPxF58RwKlZI6XY2/n597EnoOW7cKuHcaAii4vedn7Gs0RvP4PrHhF7Ao7IAmNibTOskx0bi+2wW9NpWnD6O4s3/jizduJL6NmhAc9DfDe36KHj2Dx03i159X41SiJLUbNnrhOu98+DHfTxjDn9t/RafTMnjcJKPn3Hs5DD93Nzb26ZB2t4+te+lZtzo3Yx6z/8o1Vh87w/re7VEqFHz3ZyCaVC0LDx3n24+bsalPB1J0OoZv3kOpnzscAAAgAElEQVSKVseobX/yXfuWKIDTt6I4ePXFJ+D/H/uuhFO3bEnW9WqHAhi9fR/dfKtxK+Yxf4VcZ23QWdb2SGuwfr//b8NdGUoXLcT2s+mXP689eMTv50NY37s9qTod289eJux+zEte9c2Ju3YRW1cPSrfpDwqI2L+Jot710Tx5QNyNy5gXsiclNuvYZItC9jwJOWX0fHlB48aNCQoKomfPnuj1esaPH8/atWtxdXWlYcOGL1zHzc3NMDSwWLFijB071pSRDWo2aMSFkyeY2O8z9OjpM3IMuzasw9GlBDXqNciRTC9y8mYkns4OTGjZEAWwOPAULSu5czcugdO3o166nlMBWy6Y4ARSZJ9C///opty6dSsBAQFERESwcWPal2z79u357rvv2LZtG/b29nTq1Inw8HAmTJjAmjVr8PPzIzAwEH9/f1xcXIiIiMDGxobp06dz5coV1q9fz5w5c5gzZw4BAQEsX76cbdu2sWvXLnQ6HTVq1GDUqFHMmzfPsH3AsF1I66Hr1asX1atXR6/XY29vbxi2MGfOHMOymzZtYvXq1RQuXBhHR0emTJmChcWrexeTwnJmAP5/YeWeNgHNZ+ri1yz59jgx6nMm7jmS0zGybfy7aWNGz84elMNJss972FwArsbmnkvA5QpY4j4m60zwt1XYlLQ7sFSckHUG+Nvq8oQvALg4f0QOJ8m+ygOms8E399y1ssPfacPdsjtc7m1gZ2fHieg3P2nSmHwcC9N55dacjpFt67rnzC3O/un7AOOcaH5Zv4ZRtvsy/6+GbX4kDVvjkoat8UnD1vikYWsa0rA1PmnYGp80bN+s3HNEEEIIIYQQRpFX+jmNP5NDCCGEEEIIE5AeWyGEEEKIfE6nyxs9ttKwFUIIIYTI57QyFEEIIYQQQoi3h/TYCiGEEELkcznxYwrGID22QgghhBAiT5AeWyGEEEKIfE4vk8eEEEIIIUReIEMRhBBCCCGEeItIj60QQgghRD4nPbZCCCGEEEK8RRT6vPLjwEIIIYQQ4j+ZuOeIUbY7/t16Rtnuy8hQBCGEEEKIfE5+UjefCoy4n9MRss3PpRgAJUfOzOEk2Xdr2nBqT1+S0zGyLWhEHwAuPorP4STZV7mwLQAH+zXK2SD/QqOFB6k6ZUFOx8i24DH9AXJlXU6+eSmHk2SfhVsl4uLicjpGttnZ2QGwwTf3fPV2+DuVqJTc1eBxUitov2xTTsfIto292uV0hDwl93y6hBBCCCGEUcjkMSGEEEIIId4i0mMrhBBCCJHPSY+tEEIIIYQQbxHpsRVCCCGEyOfySo+tNGyFEEIIIfK5vPKzBjIUQQghhBBC5AnSYyuEEEIIkc9pdbqcjvBGSI+tEEIIIYTIE6THVgghhBAin5PJY0IIIYQQIk/IKw1bGYoghBBCCCHyBOmxNTKdTsfaH2ZzOzwMM7Wa7l+NxNGlhKF8/69bCNzzBwoUfPBpd6r6+hEfG8tP304iKTEB2wIF6T5sBAUKFzZqToUCvvmoGRWdHNBoU/nflj3cfPjYUN7Jx4sutb1J1emYe+Bv9l+5RkErSw591YuQ6AcA7LkYyvLA04btrezelj8vhbE26KxRs2d6H8D/mtfDw6EoGq2Wb/84zJ3HsZmWKWRlydKuH9F5+WY0Wq3JsmWk0+lYMnMaN0Kvolab03/0WJxcXbMs883QwdRq0JB323xCQnwcP4wfS2JiAqkpKfQYPJTynl6mC61QUK7jEGxKlEWfmkLI2pkk3Y8wFLu+0wEHnybodXpu7V7Lg7NHUFnaUKn3OFTmlui1qVxe8Q2a2BjTRQZGv9eQco5FSdFqmfj7X9x+lF4f2lSrSNtqldHqdPx05BQBYTdxLmTH5A+bogCinsQzeedBnqammizvf62/DT1K0bRCGcbtOGCSrM/pdDq+mbuYkGs3MFermTBkACVdnAzl0+Yv5czFy9hYWwHww8RRLFi9npDw6wA8iHmMna0NP/843WR5p02bRmhoKGq1mrFjx+L6gs/el19+SYMGDfjkk0948uQJY8eOJSEhgYIFCzJmzBiKFClikrzZVaRSLbwHTOWvAU1zOgqQtg/nTJ5I+NUrqNXmDJ80hRIl3bIsM7L/5/g1bspHHTqi1WpZMGMaIRcvoNFo6N5/IHUbNTZJXgXQu2513IoWIkWrZVHASaLjEjItY2dpzpT3m/DVtr2kaHV85FWeqiWKA2Bjbk4hK0v6/LLDJHmNQa+THtts27p1K7NmzTLqa8ydO5dffvnF8Hjq1Kn0798fjUbDwIEDAfD39yc8PDzLssZ05kgAKRoNX89bzCef9WXDwnmGsrgnj/lr+zZGz13EV7N/YM33s9Hr9ez8eTUenl6M/nEhTVu3ZcuyxUbP+W4lDyzUZrRe+DPT/jjM2FaNDGXFbG3o4VedNgvX4b9sEyNaNMBcpcLTxZHtZ6/QYckGOizZYGjUAgxvXp9CVpZGz/1PDcuVwtxMRe+121lw6DiDm9TJVF67dAl+7NCSIjZWJs+W0fFDB0lJTmba0pV0HTCIlT/OybLMusULiI99Yni845ef8fSpxZSFPzFo7ASWzJxmysjYe9dDqTbnzMwBXPt1CWXb9jOUmVnZ4tK4DadnDODc3K9wb5f2mSvu24KEiGsEfzeYeycP4Nqsg0kzNy5fBgszFd1WbuWHA8cY+o6foayojRWdfLzovmor/X/5nS+a1EGtUjKkaV02n7pIz9W/cvJmBF3reJss73+tv0Ob+tK/YS2UCoXJsj534GgQyZoU1v4wncG9/Jm1ZEWm8sth4SyaOp7ls6awfNYU7GxsGNGvF8tnTWHxtAnY2lgzfkh/k+U9ePAgGo2GFStWMGjQIObMyfrZW7hwIU+epH/2VqxYQdWqVVm2bBkdOnRg/vz5JsubHRW6fIXP6MWozE1/zH2ZI/v3odEks+DnDfQZMoyFM7OeuCz78XviMuznvTu2k5qayry1v/DN3AVE3L5lsrw+bi6oVSrG7DjAuhPn+bR25s+9t4sjY1o0oGCG77Xt50KYuOsQE3cd4mFCIvMPHzdZXvFyeW4ogl6vZ/LkyTx8+JAff/wRc3Nz5s2b9/oVjST0wjmq+NQGoGylKtwIuWIosytYiIlLV2JmZkZszEOsbW1RKBRE3ryBZ620LzSPKl6Enj9n9Jw+pUpwMCStB+XM7Si8XIobyqq6FufkjQg0Wi1xyRpuPHxMBadieLo44unsyMY+HVnY+UMc7GwAaFmlHDq9noNXrxs99z95lyjOset3ALgQeY8KxYtlKtfr9Qxcv5MnT5NNni2jy2eDqeZbF4DyVTwJv3IpU/nRA/tQKpRU801viH3QsQvNP24DgFarxdzCwnSBgYJlPYm5lHbgjr1+CTu38oYybXIST2OiUVlYojK3MtzoOyHyGipLawBUljboTdxDXs21OIHhaV+O5yOiqeyUXh+qODsSfPsuKVod8ckabsc8oZyDPWXsC3Pk2TrBt+9SzdXphds2hv9af89FRDNjb4DJcmZ05sJl/GpWA8C7YnkuXQ03lOl0Om5GRDHp+wV8+uUotu3el2ndX37dSd0aVSlXOnNPnjEFBwfj6+sLgKenJ5cvX85Uvm/fPhQKBXXr1jU8d+3aNcNjb29vgoODTZY3O+Ijwgkc2S6nY2Ry/swpavnVB6Cyd1VCLl7IVH5w724USiW16tU3PHci8AjFHB0Z2e9zZk0YS92GpumtBahQ3J7giLsAhN6Poax95h55nV7P5D8OE5+sybJuLTcXEjQpnI2INklWY9Hp9Ub5Z2oma9gGBwfTrVs32rZty8GDB9m9ezf+/v6GfzExMQQFBdGuXTs6d+7Mr7/+SmBgIO3ataNr164MHDiQ2NhYgoKC6N27N/369eODDz5g4cKFhtfQ6/WMHz+epKQkZsyYgZlZ2kgLPz+/F2aKiYnh008/xd/fn44dOxISEvLG33dSYgJWNjaGx0qVEq02/bKmSmXG/m1bmDLwc2o2aARASXcPgo8eASD46BE0yU/feK5/srU0Jy7Dl6VWr0elTOv9sbWwyFSWkKyhgKUFYfdj+G5fIO2XrGfPpVAmfdiUco72fFS1IrP/PGL0zC9iY26e6cCj0+tRZejFOn4jgtgcbtQCJCbEY21ja3isVCrRPrvcfTM8jIA9u+nYp2+mdWzs7LCwtOTRwwf8MGEsXfsNNGlmMysbUpPiDY/1Oh0KpcrwOPnRPXzGraLG6CVE/LUFgJT4WIpUrInPuJWUbNaRqMCdJs1sY5G5Pmgz1Ie0sgz1WpOCraU5V6Mf0KhcKSCtB9VKbboRW/+1/u67co2cmvcRn5iErY214bFSqST12QlM0tNkOn/Uim9HDGHht+PYsGM3V6/dACAlJYVNu/bSrd1HJs2bkJCArW3mz17qs89eWFgYe/bsoW/fzJ+98uXLc/jwYQAOHz7M06fGPyb/G3cObkOXmpLTMTJJiE/A1s7O8FipVBn287XQq+zfuZOeA7/ItM6TR4+5c+smUxcsolPP3kwfO9pkea3UZiRq0vehTq/PdAXkfOS9FzZqAT72rsCm0xeNntHYtHq9Uf6ZmsmO2FZWVixZsoSYmBjatWtH+/btWbJkCVZWVowbN44jR47g6OhIcnIymzZtQq/X07RpU3755RccHR1ZtWoVCxcupFGjRkRGRvLbb7+h0WioX78+/fqlXRJdvHgxpUuXRqVSocjGJblz585hZ2fH7NmzCQsLIz4+/rXr/Ov3bW3D06REw2O9To9KlXm3N23dlobvf8ickV9x+cxpWnb2Z93c75n51WA8fWpTpJjjG8/1T/FPNdhamBseKxUKtM/G28QnJ2OToczGwpzYpKecuRVJUkragWr3xVCGNavHJ9UrU7yALes/60CJwgVJSdVy+9ETDl29YfT3AJCg0WBtrs7wPsiRD9brWNvYkpSYPn5Lp9OjenYidmjXTmLu32P8gL7ci4rETK2mmJMz1X3rcjMslO/GjqbboC+pXL2GSTOnJiWgskhvwCgUSvS6tAZMkSq1MS9QlKAxnQDwGjSDJ+EXKPluZ27tXU/UkR3YuJShcp9JnPyml8kyJyRrsMlUHxSG+pBWlqFem6uJe5rM7H1HGflufVpU9uD49Ts8TjRdIya31N+MbK2tSExK30c6vR4zVdoJj6WFOV1av4+VZdrVhVpVPQm5doNyZUpx7Mw5anhWwi7Dib8p2NjYkJiY4Zis1xs6QXbu3Mm9e/fo27cvUVFRqNVqnJ2d6d69O7NmzaJ///74+vri6Gj8Y3JuZ2NrQ2JChmOcXmfYz3t/286De9EM6dmNu5ERqNVqiru4UKBQIXwbNkKhUFDVpxa3b9wwWd6klNRMJ7EKRfbuEuBSyI5ETUqW8bgi55isx7ZGjRooFAqKFi2KnZ0dZmZmjBgxglGjRhESEmI4kytdujQAjx49wtbW1nAA8fHxITQ0FIBy5cphZmaGtbU1lpbp412aNm3KypUrsbGxydST+zINGjTAx8eH/v378+OPP6JUvvnd4V7Fk/NBxwAIv3QBlzJlDGVRt24xb9xo9Pq0Ro2ZWo1SqeDquWDqNm/B8Fk/YO/kjHsVzzee659O3oygcYW0bNVcnbhy976hLPj2XWqVLoGFmQo7C3PcixUhJPoBM9q2oGWVcgDUc3fjfMRdvv3jEB8t+JkOSzaw+dQFlh45abJGLcC5O9HULZM2EaSKswNh9003UenfqODlzemjgQCEXDiPW1l3Q9mngwYzfflqJi9cQuNWH/Bhpy5U963L7evXmPX1CL6c9A3V6774KoQxPbl2gaJV0obIFChdifjIa4ay1MQ4dCnJ6FI16FI1pCbFY2ZtS2piHNqnaQf8lLjHmFmZthETfOcu9dzTLnN7ujgSeu+hoexCZDTVSjphrlJha2FOafvChN2LoU5pVxYHnGTAL7+j0+s5dv22yfLmlvqbUdXKFQk4fgqAs5dD8ChV0lB2MyKSbkNHo9VqSUlN5czFy1R0TzvOHDt9lno+1U2e19vbm8DAtM/e+fPncXdP/+wNHjyYVatWsWTJEt5//306d+5M3bp1OXPmDK1atWLBggU4Ozvj7W26cde5VZVq1TkWcAiAi2eDKeNRzlDWd9hwFv6ykR9WrqHFR61p92kPaterj2f16gQ96xkPu3IFRyfTDQMKiX5AtRJpr+dRrAi3Yp68Zo00Xs6OnLkTZcxoJqPT6Y3y7/Wvq2PcuHF06NABf39/bt68mal85cqVtGvXjnbt2mVraKnJemzPnz8PwP3794mLi2PVqlUcPHgQgB49ehjG5D1vXBYuXJj4+Hju3buHg4MDx48fp1SpUgAv7Y318PAAYPLkybRu3ZoaNWpQu3btl2YKCgrCwcGB5cuXc+bMGb777jvWrFnzJt6uQfV6Dbh06gTfDOwL6On5v9Hs2bQeB+cSVPOrh2tZd74Z+DkKhQLPWnUo712N6Ig7LJ06JW0/2NvTY/ioN5rpRXZfvEp9dze29uuMAgVfbf6D3vVqcvPhI/68HM6KwNNs/rwTSoWCmXuPkJyqZdruw8z8pAX+vlVJ1KQwYsseo+d8nYNXr1OrlAs/df0QBQom7zpIJx9P7jyKJSDs5us3YCK1GzXm7IkgRn2WVvcHjhnPb+vWUryEK7UaNHzhOmsXzEOTrGH5d2kTMa1tbRk18zuTZX4QHECRCjWp9tU8UCgIWT2dEk3bkXQ/gofnjhJX4QrV/7cAvV7Pk/DzPLp8koTI65TvOhznBh+hUJkRsnamyfICHLhyjTqlXVnVrQ0oYPyOA3St7c3tmCccCr3BLyfOsaJbaxQKmHcwCI1Wy42Hj5jwfmNStFrC78cwdbfpxq7mlvqbUVO/2hw7HYz/lyPT5jgMG8TqzdtxdXGisW8tWjZpQNfBIzEzU/HBO41wf9bwvXEnkg+amW4M5XONGzcmKCiInj17GoavrV27FldXVxo2fPFnz83NjfHjxwNQrFgxxo4da8rIuVL9ps04efQoA7p0RI+eEZOnsnHVClxKuuHXuMkL13n/k/bMmTSBfp07gF7P0HETTJb3+I0IvJwdmfx+YxQKBQsOn6BVFQ/uxsZz6tbLG67OBe04F5m7x9bmtH379qHRaNiwYQPBwcFMmzbN0Dl5+/ZtfvvtNzZt2oRCoaBz58688847VKhQ4aXbU+j1xr/OtXXrVnbu3ElKSgqJiYkMGzaM9evXc/36daytrSlQoADVqlWjevXqrF+/3jBL9ejRo/zwww8oFAoKFizI1KlTCQ0NzbSMn58fgYGBzJ07F3t7ezp1SrsUeuLECYYNG8bWrVv56KOPCAwMxN/fnwkTJrBr1y7s7e1p0aIFQ4YMISkpCaVSyYABA6hXr94r30tgxP1Xlr9N/FzSJp6UHGnaxsT/x61pw6k9fUlOx8i2oBF9ALj46M0PYzGWyoXTxhce7NcoZ4P8C40WHqTqlAU5HSPbgsekzfLPjXU5+eal1yz59rBwq0RcXFxOx8g2u2djTjf45p47bXb4O5WolLd7OMw/OakVtF+2KadjZNvGXm/HxL++63cZZbuLOrZ8ZfnUqVPx8vKiVatWANSvX5+AgLQOhZSUFOLi4gy31/vkk0+YOXOm4er+i5jk09WmTRvatGmT6bnns1L/KWMPa926dTPNTH1ennGZ55eUBg0alGk5Hx8fw2D/58s8743NuOzKlSv/zVsRQgghhBDZtGHDBjZs2GB43KFDBzp0SL/1Y3x8fKYJnSpV2kRDMzMz1Go1RYoUQa/XM2PGDCpVqvTKRi3IDzQIIYQQQuR7xro1V4eOmRuy/2Rra0tCxomGuvSJhgDJycmMHj0aGxsbw5CgV8lz97EVQgghhBD/Tk7dx7Z69eqGK+zBwcGUK5c+0VCv19O/f3/Kly/PpEmTUKlUL9uMgfTYCiGEEEKIHNGsWTMCAwPp2LEjer2eb7/9lhUrVlCyZEl0Oh3Hjx9Ho9EYxt0OHTqUatWqvXR70rAVQgghhMjncuJXwiDtbliTJk3K9FzZsmUNfz+/q1a2t/dGUgkhhBBCCJHDpMdWCCGEECKf0+l0OR3hjZCGrRBCCCFEPpdTQxHeNBmKIIQQQggh8gTpsRVCCCGEyOekx1YIIYQQQoi3iPTYCiGEEELkc3mlx1YatkIIIYQQ+ZxOlzcatgq9Po800YUQQgghxH/ScfkWo2x3fc+2Rtnuy0iPrRBCCCFEPqfTy31s86UT0Y9yOkK2+TgWBqDpD6tyOEn27R/cjZ4/78jpGNm2vMsHAByLepjDSbKvjlNRAMLik3M4Sfa521oQd/FoTsfINrvKdQEo/fXsHE6Sfde/GQbAk+BDOZwk+wpWbZgrj8lRKbnnQqmTWsEG39zVVOjwdyqdV27N6RjZtq57m5yOkKfkrtoqhBBCCCHeuLwyeUxu9yWEEEIIIfIE6bEVQgghhMjn8spdEaRhK4QQQgiRz+WVm2TJUAQhhBBCCJEnSI+tEEIIIUQ+J5PHhBBCCCGEeItIj60QQgghRD6nlcljQgghhBAiL5ChCEIIIYQQQrxFpMdWCCGEECKfk9t9CSGEEEII8RaRHlshhBBCiHwur4yxlYatCel0OlZ+N5Nb4aGYqdX0/t9oipdwzbLMrBFDqVGvAU0/apMjORXA4CZ1KGtfGI1Wx+x9R4l8EpdpmYJWFvzYviW9124nRavD0syM0S3qU8DSgqSUVKbtDeBJUrJJM3et5YlroQKk6nSsPHaWe/GJmZaxszBndHM/xu48RKpOh425ms/qVsNKrSY+WcPKoLPEJWtMklen07F6zixuhYeiVpvTc/goHEuUMJTv27aFI7t3gkLBx5/2pGpdP+JjY1n8zUSSEhKwLViAnl+NpEDhIibJ+zzzgmnfcP1qCGpzc74YOwFn15JZlpkweAB1Gjam5SftDc/fvn6dod268POff2FuYWGyvNOWrCH0xm3UajPG9u+Bq5OjoTzw9Dl+2rAdgApl3BjRxx+FQgHAX8dOse/vE3wzpK/RcyoUMPnDd6hYvBiaVC0jt+3lZsxjQ3nHmp50quWFVqtj3sEgDoRcw0ptxpSP3sG1cEHUKhUTfj/A2Tt3AbBUm7GmxyeM2LqXaw9ijJ5fp9Mxfdk6Qm/ewVxtxteff4prcQdD+dEz51m6+XcAypcuyf96dSYhKYlxc5eTkJRESqqWLz9th1e5skbP+qLsueGYrNPpmDN5IuFXr6BWmzN80hRKlHTLsszI/p/j17gpH3XoiFarZcGMaYRcvIBGo6F7/4HUbdQ4R/K/SJFKtfAeMJW/BjTN6ShA2ndID9+quBUuSIpOx0+Bp4mOS8i0jJ2FORNbNWLE9n2kaHV84FkOb5e0Y4q1uZpCVpb037ArB9K/GXmlYWu0oQjJyck0adLEWJvPwt/fn/DwcAASEhLo2rUrS5Ys4fLly8ybNw8APz+/LMua0qmAQ6RokpmwcCkdPx/Auvk/Zllm09LFJMTGmjxbRn5lS2KuUjFo4x8sDTxF3/o1M5XXLOnM9I+bUdjK0vBcyyoehN57yJebd3Pw6nW6+niZNHM11+KolSq+3RvI5jOX6VC9cqbyyk7FGNqkDgWs0htVrSp7EHo/hql/BrL/6nXaVq1gsrynjxwmRaNh3IKfaNenH78sTK8LcY8fs3/7VsbMX8KI735k1ZyZ6PV6dqxdRTlPL8bMW0Sz1u3Y9NNik+UF+PvgATTJycxeuZbugwazdM6sLMusWTCXuCdPMj2XGB/P0jmzUJurTRUVgIPHT6NJSWHFtDEM6tqOOSvXG8oSkpL4YdVGvv/6S1ZOH4uTgz2PY9NO3mYt+5l5P2822e+mN6/ojoWZiraLf2H63gC+btnQUGZva00332q0W7yebiu3MLx5PcxVKvrU9+Fq9EPa/7SBUdv2Usa+MACeLo5s+KwDbkUKmSQ7wKETwWhSUlg+ZSQDOrXhhzWbDGUJSU/5ce0WvhsxkOXfjMKpWFEex8Wz7vd9+FSpwOIJwxnfvzszl60zWd6Mcssx+cj+fWg0ySz4eQN9hgxj4czpWZZZ9uP3mT57e3dsJzU1lXlrf+GbuQuIuH3LlJFfqUKXr/AZvRiVueXrFzaRmiWdUatUjN91iPWnLtDFxzNTuZezA6Oa16OAZfp3yI7zV5myO4ApuwOISUhiUcBJU8cWL5DnxtjGx8fTu3dv3nvvPfr06UPFihUZOHBgTscCIOT8Wbxq+wLgXrkK10OuZCo/fvAASoXCsExO8XR24MTNCAAu331AeUf7TOV69Pxv25+Zeje3Bl/m5xPnAXCws+FR0lPTBQY8ihXhQtQ9AK49fEypogUzlev1embt/5uE5BTDc84FbTkfmbZO6P0YPIoVNVneq+fP4lmrNpC1LtgVKsSUpaswMzPjSUwM1ra2KBQKIm/ewKt2HQA8PL0IPX/WZHkBLgWfoUbdtJPDCp7ehF26lKn8yL69KJRKavrVMzyn1+uZ+80kug38AgtLK5PmDb4cim+1tC8nz/JluRx+w1B27koY7m4lmLNyPb2//paihQpQuGABALzKuzOqz6cmy1nTzYVDV9OyBd+OwtMlvVe5agknTt2KRKPVEpes4WbMYyoUt6eBRyk0Wi2rurdlUOM6HA5NW99cpaLvz78Rft/4PbXPBYeE4euddiLpWa4Ml8NvGsrOhYTjXtKF79ds5rPxMyhSqACFC9jRqdU7tG7WAIBUrQ5zE5/0PJdbjsnnz5yill99ACp7VyXk4oVM5Qf37kahVFKrXn3DcycCj1DM0ZGR/T5n1oSx1G349vTWxkeEEziyXU7HyKS8Y1HORUQDEHb/EWWKFs5UrtPDt3uPkKDJelXPp6QzCZoUzj37PsmtdHq9Uf6Z2htt2CYkJNCvXz+6dOnChAkTgLTe0S+++ILu3bsTFxfH4MGD6dmzJ61bt2bdurSz9HPnztG2bVs+/fRThgwZwtXP3xAAACAASURBVMiRIwFYvnw5bdu2pUOHDsycOROAuXPnMmLECHr37k3Lli0JCAgwvH5cXBw9evSgffv2dOnSBYCgoCCGDBnywrynTp2iffv2dO7cmb59+xIfH/8md0cWSQkJWNvYGB4rlUq0qakA3L4WztE/99C2Vx+jZsgOa3N1pgagVq9D+ewSLcCpW1HEPs06zECn1zOrTXM+9q5I0PU7Jsn6nJXajKSU1AxZyJT50t0HJGhSMq1z61EsVUsUB6BqieKYm6lMExZISkjEytbW8FipVBnqAoDKzIw/t25mUv/PqPnsC6mkuwenA48AcCYwgORk0w31gLSeV5tMmdPr742wUA7u/oOufQdkWmfdkoX41KtPmXLlTZoVICExCVvr9Ma0UqkkVasF4HFsPKcuXGaQf3t+HDOUdb//yc3ItEv5zevVThsfYCJ2lhbEZfh/qdXpUSnTXt/Wwpy4DJ+1hGQNdpYWFLa2oqCVJd1WbmH/lWuMfi+tl/fUrUii/jFsyNgSEp/+Yz8rDPv5SVw8Jy+GMLBLG34Y9QXrd+3nZmQ0djbWWJqb8+DxE8bPW8aATq1Nmvm53HJMTohPwNbOzvBYqVSR+izntdCr7N+5k54Dv8i0zpNHj7lz6yZTFyyiU8/eTB872qSZX+XOwW3oUlNev6AJWanVJGb4jtDp9Zm+Qy5E3SP+JUPVPvQqz9bgy0bPKLLnjY6x3bZtG+XKlWPIkCGcPXuWoKAgAD744AOaNWvGxYsXadWqFc2bNyc6Ohp/f386d+7M+PHjmTFjBh4eHsyZM4fo6GhCQkL4448/WL9+PWZmZgwaNIi//voLAHNzc5YuXUpgYCDLly+nfv20s9Thw4djb29PdHR0tvLu27ePZs2a0atXLw4cOEBsbCy2Gb643zQrGxuSEtPHfer0OlRmaf8LjuzZxaMH9/n2ywE8uBuFmZka++JOeOdAT0GiJgUr8/SqoUSR7bOur7buxbVwAb798B38V201VsQsklJSsczQMFUoXj9eaNfFUDrXrMJXTepwPuoeMYlJxo5pYGVjzdMMdUGvS68LzzVr8wmNP/iI2SOGcvnMKd7v4s/aH+cwfegXeNaqQxEHh39u1qisbW1JSnhx/T2wcwcP70Uzum9voiMjMVOrcXB25q9dO7F3dGTv9m08eviAMQM+Z8bSlSbJa2NtRWKGKwd6nR4zVVodKWhnSyX30tgXTuvZr16pHFev38LNubhJsmUU9zQZW3Nzw2OlQmH4BaD4ZA02FullNhbmxD5N5nFiEvsuhwGw70o4fRvWMm3oDGysLUnI0PjW6zPuZxsqlS2FfaG0/VytogdXb97GzdmRsFt3+PqHnxjc9ROqVzL9iQ/knmOyja0NiQnp4z11eh1mz3Lu/W07D+5FM6RnN+5GRqBWqynu4kKBQoXwbdgIhUJBVZ9a3L5xw+S5c5OklBQs1enHYIUie997LgXtSNRosozHzY10Ol1OR3gj3mjDNjQ01NDI9Pb2NnzwSpcuDYC9vT2rVq1i79692NraGs447927h4eHBwA1atRg165dXLt2DW9vb9TqtEtUNWvWJDQ0FICKFSsCULx4cTQZLgsMGzaMevXq0bZtW6pXr06tWq8+2Pft25dFixbRrVs3HB0d8fIy7rjQclW8OHP0CHWavEPYxQu4lkmfLNGp3yDD31uW/0ShokVz5AAKaWemvqVdORR6k4rF7bn+8NFr1+lUswr34xPZd+UaT1NS0elN+wEJu/8IbxdHTtyKokzRQkQ8fn2vVTmHohy9focr0Q+p4epEmAkv33pU8SL4aCC1Gzcl7OIFSmSoC1G3brLpp0UMmvQtKjMzzNTmKBRKQs4GU+/d96hYrQYnDv1FuSqmHcdcybsqQYcPUb/5u1w5f5ZS7h6Gsp6Dhxr+/nnxAgoXtadm3Xos3b7T8HyP91swZb7pxgV7V/Ag4GQwzfxqcT4kHHe39Ml5Fcu6EX4rgsexcdjaWHP+6jU+fqfhK7ZmPKduRdK0Qhl2XrhKVVcnQqIfGMqC70QxrJkf5mYqLFQq3IsVJST6ASdvRtK4fBkuRN6jdukShGZYx9S8y5cl4NQ5mvnW5PzVa5Qt6WIoq1DGjfDb6fv5Qug1Pm5Sn2t3Ihk1ZzHfDO5DuVKur9i6ceWWY3KVatU5evAvGrd4j4tngynjUc5Q1nfYcMPfK+bPpYh9MWrXq0/ErZsEHT5Mw2bvEnblCo5OTjkRPdcIufeQ6q5OBN2IwL1YYW4/evL6lYAqzg6cvZO9zjRhGm+0YVumTBmCg4N55513uHTpkqHh+nym8fLly6latSqdO3fm2LFjHDp0CEhroIaFheHu7s7Zs2cN21qxYgWpqamoVCpOnDjBxx9/zJUrVwzb+ycPDw9sbW2ZPn06X375JVu2bHll3h07dtC6dWtGjBjB4sWL2bhxo1HH49Zs0IgLJ08wsd9n6NHTZ+QYdm1Yh6NLCWrUa2C01/23joTdokZJZ35s9x4KBcz4M5BPqlUi4nEcf1+//cJ1dl8K43/N6vFeZQ9UCgUz/gw0aebTt6Oo5GTP6OZpY0CXHztL8wpluBeXQHDEiw86d2Pj6V23GgCPEp+y4pjpxqzWqN+QiydPMHlAH/R6Pb1HfM3ujb/g4FKC6n71cS3rzuT+fUABXrV9qVC1GtF37rBk6iQACtsXo9f/THtp0bdxU84EHWNYD3/Q6/ly/GS2rV2Nk6srdd6i8XvPNa5dnaCzF+k5agp6PYwf2Iu1v+3BtbgDDWtVY0DXTxg4aTYA79T1ydTwNaU9l0Kp5+7G5j6dUChg+JY99PKrwc2Hj9l3JZxVf59h42cdUSoUzPrzCJpULfMPBTGtdXO2fN6JVK2OoZv/yJHsAI18qhF07jK9xk5Dr4dx/brx8+9/4lq8GA1qVmVAp9Z88e0PADT1rUnZki58NXM+mpRUvlu1AQBbaytmDR/wqpcxitxyTK7ftBknjx5lQJeO6NEzYvJUNq5agUtJN/wav3iS9vuftGfOpAn069wB9HqGjptg2tC5zMmbkXg6OzChZUMUwOLAU7Ss5M7duARO34566XpOBWwN8ztyu7xyVwSF/g3+1ERqaiqjRo3izp07lClThpMnT+Lg4MCECRMoW7Ysx44dY8KECRQuXJhChQoRGhrKrl27uHLlClOmTMHa2hq1Wo2joyNTpkxhxYoV7Nq1C51OR40aNRg1ahTz5s3D3t6eTp06ER4ezoQJE1izZg3+/v6G1wFYsGABx48f5/PPP2fjxo3MmTMHPz8/AgMDDcvGx8czceJEw+tOmjQJV9dX9x6ciH597+XbwscxbfB70x9W5XCS7Ns/uBs9f96R0zGybXmXDwA4FvUwh5NkXx2ntElyYfGmHaP7/+Fua0HcxaM5HSPb7CrXBaD017NzOEn2Xf9mGABPgg/lcJLsK1i1Ya48Jkel5J4GhJNawQbf3HVn0A5/p9J5pemGwv1/reueM7eR+6f6s5cZZbsBw3oZZbsv80Zrq5mZmWGS14vUqVOH3bt3Z3n+/PnzLFq0iCJFijBnzhzD8IMePXrQo0ePTMsOGpR+eahs2bKsWbMGwPDf5/r370///v0B8PVNu3wUGBiYZdmtW3NP5RdCCCGEEC/3VpyGFS1alJ49e2JtbY2dnR3Tpk3L6UhCCCGEEPmGqe7dbWxvRcO2RYsWtGjRIqdjCCGEEEKIXOytaNgKIYQQQoick1cmj0nDVgghhBAin8srDds895O6QgghhBAif5IeWyGEEEKIfC6vTB6THlshhBBCCJEnSI+tEEIIIUQ+l1fG2ErDVgghhBAin8srDVsZiiCEEEIIIfIE6bEVQgghhMjn9NJjK4QQQgghxNtDoc8rTXQhhBBCCPGfeE6aZ5Ttnh830CjbfRkZivAvDft1X05HyLbZH78DQNdV23I4Sfat7daaS48TcjpGtlUqZAPAgE27czhJ9s1v1wLIffWi9NezczpGtl3/ZhgAp6d+lsNJsq/6qJ8A2BMekcNJsu/dsi50Xrk1p2Nk27rubQBov2xTDifJvo292uWqfQxp+3mDb+5p3nT4OzWnIwAyeUwIIYQQQoi3Su45pRFCCCGEEEYhPbZCCCGEEEK8RaTHVgghhBAin8sr9xKQHlshhBBCCJEnSI+tEEIIIUQ+l1fG2ErDVgghhBAin9Pp8kbDVoYiCCGEEEKIPEF6bIUQQggh8jmtXpfTEd4I6bEVQgghhBB5gvTYCiGEEELkc3nldl/SsBVCCCGEyOdk8pgQQgghhBBvEemxNTIF0Ma7As4FbUnV6dl45hIPE5IM5Q3KlqSqiyMAV6IfsDfkOuYqJV1qemKtNkOj1bLu1EUSNCkmzdy9TlVKFi5Iqk7L0qNniI5LyLSMnYU541s2ZNT2/aTodFipzRjYwAcLtRmpWh0LA07y5GmyyTLrdDoWz5jKjdCrqM3NGTB6LE6uJbMsM2XoF9Rq0IgWbT4hIT6O78ePITEhgdSUFHp8OZQKnt4myasAOlSvhEuhAqRqdaw7eYH7CYmG8sYebtR0dQLg4t377LoUbijzdnagmmtxVgadM0nWjJlzQ71QKGDyh+9QsXgxNKlaRm7by82Yx4byjjU96VTLC61Wx7yDQRwIuUZBK0sODOnJ1egHAOy5FMrKv8/weX0fPvSuQPxTDYsDTnAg5JpRs//jneD6bhesHEugT03l1h+rSH5031DqUKs5RSrVQo+eu0d38eTqGRNmS6fT6dg0/wcirodjplbTafBXFHN2MZQf3vErx/ftAQW06PQpVWr7otNq2fbTQm6FhpCaksJ7XbpRpbavSfIqgB6+VXErXJAUnY6fAk+/sB5PbNWIEdv3kaLV8YFnObyfHaetzdUUsrKk/4ZdJsn7PHPvutVxK1qIFK2WRQEns2a2NGfK+034atteUrQ6PvIqT9USxQGwMTenkJUlfX7ZYdLMuW0/v06RSrXwHjCVvwY0zekoRpFX7mP7n3psBw4cCMC5c+do1aoVs2fP5sSJE1y5cgWAJk2akJyc/uUVHh6Ov7///yvo1q1bmTVrVpbn27dvz507d7KUr1q1io4dOxIbG8uQIUPQaDSMHDmSw4cPv3RbxlDFqRhqlZK5h0+y82IoH1YpZygrYm1F9RLFmXv4BHMPn6CcQ1GcCthSu5QLdx7HMv/IKYIjonmnfGmTZH2uRkln1ColE/84xPpTF+lc0zNTuaezAyOa+VHQ0sLwXAN3N24/jmXK7gCO3YigVRUPk2YOOvQXKRoN05etwr//IFb8MCfLMusWzSc+Ntbw+Ld1a/GqWYtvFi3li3ET+WnmdJPl9XJxRK1SMfvAMbafD6GNd3lDWVEbK3xKOjPrwDFmHThGBUd7nAvaAvBJ1Qp86FkOBQqTZX0ut9SL5hXdsTBT0XbxL0zfG8DXLRsayuxtrenmW412i9fTbeUWhjevh7lKRRVnB3acu0KnZRvptGwjK/8+Q3lHez7yrkDrRevwX7mZIe/UxVJtur6AQuWqojRTc3X1NCIObsWlSXtDmcrCCoeaTQhZPZWw9XMo8U4Hk+X6p/N/HyElRcPQ7+bxQY/P2LZ0oaEs/skTjuzczpDZcxn47Ww2zv8evV7PiQN/otWmMmT2XD4bN5n7UREmy1uzpDNqlYrxuw6x/tQFuvhkrsdezg6Mal6PAhnq8Y7zV5myO4ApuwOISUhiUcBJk+UF8HFzQa1SMWbHAdadOM+ntTOfgHu7ODKmRQMKWlkantt+LoSJuw4xcdchHiYkMv/wcZNmzo37+VUqdPkKn9GLUZlbvn5hkaP+U8N23rx5ABw5coSOHTsybNgwtmzZwr17995ouP9q6dKl/PXXXyxfvpwCBQowZ84czM3NcyRL6aKFuBL9EIBbj2JxLWRnKHuc9JSf/j6DHtADKqWCFK2OgPDb7Au5DkAhK0vikzUmzVzeoSjnIqIBCH/wiNL2hTKV6/V6pv0ZSLwmPdftR08MX/pWajO0Jh6rc/lsMNXq1AWgvKcX4VcuZSo/un8fCqWS6r51Dc992KkrzVu3BUCr1aI2YR0pa1+YS3fTet9uxDyhZJGChrJHiU+ZH3AyU71I1abdhuXag8esP33pBVs0vtxSL2q6uXDo6g0Agm9H4fmsBwigagknTt2KRKPVEpes4WbMYyoUt6eKsyOVnR1Y37s98zu+TzE7G9yLFeHY9TtoUrVoUrXcePCYCsWLGT3/czauHsReuwBAYuQ1rJ3cDGXaFA2a2BiU5hYo1RaQgz0t4RcvULGGDwClK1TidmiIocy2YEFGzF+KysyM2EcxWNnYolAouHz6BIXsi7Fo/CjW/zjbZL21AOUd0+tx2P1HlClaOFO5Tg/f7j1CgibrcdenpDMJmhTORZr2u65CcXuCI+4CEHo/hrL2RTKV6/R6Jv9x+IXfFbXcXEjQpHD22Xs2ldy4n18lPiKcwJHtcjqGUen0eqP8M7Vsdz9s3bqVLVu2oNPpuH79OkuWLGHz5s2o1WpSUlIICAjg4sWLuLu7v3I7x48fZ86cOahUKlxdXZk0aRI7duwwbPuLL74gPDycvXv3kpqaip2dHXPnzs20jTlz5hAQEEDx4sV59OhRprJFixZx8uRJlixZYmjMNmnShD/++CNLluTkZAYPHkx8fDxPnz5l+PDh1K5dO7u7JFsszcx4mpJqeKzTg1KhMPwPfz7E4IPKHkQ8juPBs8vReqCvX3WcCtiy+OjpN5rpdazUZiRmzKzTGzIDXIi6n2Wd+GQNns4OTP+oKTYW5kz+47DJ8gIkJSRgbWtreKxUqtCmpqIyM+NmeBiH9/7B/6bOZOOyJYZlbOzSTjIePXzA9+PH0HPIVybLa2mmIilTvdC/sF609irPnUdx3ItPqxen79zFo1iRF27T2HJLvbCztCAuwxUjrU6PSqlAq9Nja2FOXIahEAnJGuwsLbj2IIbv90cTGH6Lj7wrMPH9JszeF0i/hrWxMVejVqmoXtKZX06YbviHytwSbXL6sCV0OlAo4dm9JjWxMVT6bCIolET/nfX4ZipPExOxsrYxPFYqVWi1WlQqFQAqlYrDO7axa+0qGn7YGoCEJ7Hcj7jD5xO+JezCOdZ9N4PBM38wSV4rtZrEDEO7Mn72AC5Evbwx9aFXeeYdMm3PJzz77L0i8/lXNAA/9q7AD38dM3rGf8qN+/lV7hzchnVxt9cvmIvllclj/+q6WoECBVi4cCF+fn54eXnRunVr7O3t6dSpE1evXqVly5Y4OzsD0LNnT5TKtA7hpKQkrKys0Ov1jB07lnXr1lG0aFG+//57tm3bhpmZmWHbOp2OU6dOsXLlSpRKJb169eL8+fOGDFevXuXEiRNs3ryZxMREmjdvbijbsWMHbm5uxMbGZuu2Fbdu3eLBgwesXLmShw8fcuPGjX+zO7LlaWoqFmYqw2OFIvM4FjOlkg7VKpGcmsqWs1cyrbso8DQOttb08q3K1D+PvvFsL5OUkoqVWXrVyHgwepnW3hXZeSGUA1dv4Fq4AIMb1Wb0jgPGjmpgZWPD08T08Vt6nQ7Vs/dwcNfvxNy/z7gBn3MvKhIzMzUOTk5U9/XjZlgos8eMotsXQ6hSvYbJ8j5N1WKZYR8rUGSpF119qpCcomX96Ysmy/UquaVexD1NxjZD77tSoTD0FMcna7CxSC+zsTAn9mkywbejDCcaey6FMaSpH+H3Y1h97AwrurXlZswjzt6J4lFiEqai1TxFmfGyZ4ZGbcEyVVDbFuTCglEAuHf8kvg7YSRG3TBZvucsra15mpS+X3Q6naFR+1yDD1pTt8X7LBo3kqtnz2BToACVa/miUCjw8PTmXuQdk+VNSknJNKREkY16DOBS0I5EjSbLOFFTSEpJxSpT5uyNh3QpZEeiJiWHMue+/Szyhn81FKF06eyP9Vy+fDlr1qxhzZo1TJ+eNnYxJiaGe/fu8eWXX+Lv709gYCCRkZGZtq1UKlGr1QwdOpTRo0dz9+5dUlPTe4nCwsKoUqUKSqUSW1tbypVLH7NasWJFVq5cia+vL5MmTXptRg8PD7p06cLQoUOZOHEiOt2b/9WN6zFPqFjcHoCShQsQFRufqbxnbW8iY+PYfPYKzz/yTTxKUcM1bdC/Rqs1+b3lrt57iHeJtMu3Ze0Lc/vRk9euk6DRGM7OY5OSsTI37bzEil5VOXU0EICQ8+comeHKQbdBXzJj+WqmLPyJJq0+4MPOXanu68fta9eYOXoEQyZ9S426fibNe+3BIyo7pV3WLlWkIJFP4jKVf+5XnYjHcfxy+iJvyzl0bqkXp25F0ujZuPSqrk6EPJsQBhB8JwofNxfMzVTYWZjjXqwoIdEPmNb6Xd6rnDb+169sSS5ERlPE2orC1la0/2k9k37/C6eCdpm2ZWwJd8IoWDZtXKK1cxmS7qc3/lKfJqJLTUGvTUWvTUX7NAmVpbXJsmVUplIVLp0MAuD6lUs4lypjKIu+c4ulU8ah1+tRmZlhplajUCopUzl9nYhr4RQu5mCyvCH3HhomVbkXy149Bqji7MDZO6a9nP9cSPQDqpVIm0zqUawIt2Kyl9nL2ZEzd6KMGe2lcuN+zu/y3VAEwNAD+yIKheK1DbDChQtTvHhxFixYgJ2dHfv378fa2pqoqCjDtq9cucK+ffvYtGkTSUlJtGnTJtN2S5cuzerVq9HpdDx9+pSwsDBDmbu7O0qlkiFDhtCxY0d+/fVXPv7445fmCQkJISEhgSVLlnDv3j06duxI48aNs7s7suVC5D3KFSvCoPo1QQEbTl+iQdmSPExIRKFQUMa+ECqVkgqOaY3fXRfDOH4rkk7VK1HLzQUlmHxM5clbkVRxdmDcew1QoGBJ4Cneq+ROdFw8p2/ffeE6m89cpnfdarxToQwqhZJlR007Q7t2o8YEHz/GyN7d0ev1DBo7ge3r1uJUwpVaDRq+cJ01C+ai0SSzbM5MAKxtbBk9K+ukM2M4GxFNBceiDGtcGxQK1p44TxOPUtyPT0CpUOBRrDBmSiWVno3p/O38Va5nmNmfE3JLvdhzKZR67m5s7tMJhQKGb9lDL78a3Hz4mH1Xwln19xk2ftYRpULBrD+PoEnVMn3vYWa0eZeutauSmJLCyG17iUlMomSRgvzarwspWi1Tdx8y6UH6ccgZ7EpVopz/CFAouPn7Shx8mpH86B5Pws6SGFWR8t1GodfrSbgdRtz1nBl77VW3HiFnTvHdsIGghy5D/seBrZso5uyMZx0/XEqX5buhA1EoFFSsWQsPT29KVajIxnnfM3vIANDr6TBwiMnynrwZiaezAxNaNkQBLA48RctK7tyNS+D07Zc3Ap0K2L7y8rkxHb8RgZezI5Pfb4xCoWDB4RO0quLB3dh4Tt16eWbngnaci8yZRmJu3M8ib3hj3Sfe3t7MmjWLEiVKvHQZpVLJ119/TZ8+fdDr9djY2DBjxgyiotIruZubG1ZWVrRp0wZzc3OKFSuWaVJaxYoVadGiBZ988gkODg4ULVo0y+uYm5sza9YsunbtSpUqVV6ap1SpUsyfP59ff/0VtVrNF1988R/f/cvpIcsQg3vxtwx/j9zx1wvX++nv4DeeJbv0wIpjmV8/6lJYluWGbNlr+Ptx0lNm7f/b2NFeSqlU0m/k15meK1Eq6xWGjp/1Nfxtqkbsi+jJesKS8dLbl1v/fOm6ofdjCL0fY6xoL5Vb6oVeD2O278v03LUH6ftr/cnzrD95PlP5nUexdF62Kcu2vv7HdkxLz+09azM9cy8m/QQiKuA3ogJ+M3WoLJRKJR0GZW6YOma41d57XbrxXpdumcrVanO6DPmfSfL9kx5Y/o/ja+STrPV48OY9mR6vDDprzFivpAd++sdci39e5QEYuDHzrbGW/Z0zt4CD3LmfXyfx7k32fWbaq3umlFdu95Xthm2bNm0MfwcGpl3yHTRokOG5jh070rFjRwAOHMg8hq5s2bKsWbMGgHr16lGvXr2XbtvKyorVq1e/Mkv37t3p3r17puf+2aAuU6YMR48ezZRn2rRpWbb1448/vvK1hBBCCCFE7iA/0CCEEEIIkc/lux5bIYQQQgiRN5l6orqx/KcfaBBCCCGEEOJtIz22QgghhBD5nNYItzzNCdJjK4QQQggh8gTpsRVCCCGEyOdk8pgQQgghhMgT8krDVoYiCCGEEEKIPEF6bIUQQggh8jm53ZcQQgghhBBvEemxFUIIIYTI53S6nOmx1el0TJgwgZCQEMzNzZkyZQpubm6G8o0bN7J+/XrMzMzo168fjRs3fuX2pGErhBBCCJHP5dTksX379qHRaNiwYQPBwcFMmzaNhQsXAnD//n3WrFnDli1bSE5OpnPnzvj5+WFubv7S7Sn0eWVQhRBCCCGE+E/UfccaZbspiya/snzq1Kl4eXnRqlUrAOrXr09AQAAA+/fv59ChQ0yaNAmAAQMG8Pnnn+Pl5fXS7UmPrRBCCCFEPve6Buh/tWHDBjZs2GB43KFDBzp06GB4HB8fj62treHx/7V371FRV+v/wN+DIF5ARQghUEHECymWImRppZJyNJG8IArqEUsNOYgWX1ATLxgEiBdKFD2SRoKAtywpC1JRTipQCatCFONyEuWqNIDDwMzvD9ZnvqDMhe/vHPf+6PNay5XMrNV5H5Ph+ez97Gd369YNLS0t0NfXh1QqhbGxseq93r17QyqVavzfo8KWEEIIIYT8VzxayD7KyMgIDQ0Nqq8VCgX09fU7fa+hoaFDodsZmopACCGEEEKYGDt2LLKysgAAv/zyC4YNG6Z6z9HREXl5eZDJZPjrr79QXFzc4f3OUI8tIYQQQghhQpiKUFRUBKVSifDwcGRlZWHQoEGYOnUqUlNTkZKSAqVSiZUrV2L69Oka/31U2BJCCCGEkKcCtSIQQgghhJCnAhW2hBBCCCHkqUCFLSGEEEIIeSpQYUsIIYQQQp4KVNiSp1pO1RRWbgAAIABJREFUTg7rCF3y119/sY5AyDNNoVCgtbUVubm5aG5uZh2HENJFNBWBEalUiqysrA4fnB4eHgwTqffpp5+qfc/f3/8JJuk6b29vHD16lHUMnS1cuBDJycmsY3TJ77//jpSUFMhkMtVrERERDBNplp2djc8++6zD997nn3/OMJF6p0+fVvser58XgsrKStTX16Nbt244ePAgFi9ejJEjR7KOpVF0dDQGDhyIO3fu4Ndff4WZmRkiIyNZx9Lo5s2bkEql0NPTw86dO7Fq1SpMmDCBdSy1GhsbUV9fD319faSkpMDDwwNWVlasY2mUk5ODpqYmKJVKhIWFYc2aNZg1axbrWEQNunmMET8/P5ibm8PS0hIAIJFIGCdSz8zMDACQkZEBa2trjB07FgUFBaioqGCcTDs9PT0EBATA1tYWenptGxRr1qxhnEq9vn374siRIx3yTpw4kXEqzUJCQuDj4wMLCwvWUXQSERGBDRs2iCJvcXExgLah5T179sRLL72EgoICtLS0cF/YBgcHY+XKlUhKSsL06dMRHh6OxMRE1rE0ysvLQ1BQEBYvXozExEQsXbqUdSStNm/ejI0bN+KTTz7B2rVrER0dzXVh+8EHH2DOnDn47rvvMHToUISGhuLQoUOsY2kUHR2NHTt2YOvWrUhOTkZgYCAVthyjwpYRpVKJHTt2sI6hEy8vLwDA999/jy1btgAA3N3dsWzZMoapdOPu7s46QpeYmJigsLAQhYWFqtd4L2zNzMwwf/581jF0ZmlpiVdeeYV1DJ28//77AIDly5fjwIEDqtd9fX1ZRdJZS0sLxo8fj/3792PmzJlISkpiHUkrhUKB/Px8WFtbo7m5GbW1tawjaaWvrw97e3vI5XK8+OKLaG1tZR1Jo/r6ekydOhWJiYmIiorCpUuXWEfSytDQEKamptDX18dzzz1HLSqco8KWkeHDh+P69esdtua6d+/OMJF2dXV1KCsrw6BBg3D79m1IpVLWkbSaM2cOvvzyS9y5cwcvv/wyhg4dyjqSRhERESgqKsKtW7dga2vL/dYtAFhZWeHAgQMYOXKkaueB52Lc1NQUoaGhcHBwUOXVdI85D2pra1FfX48+ffqgrq4O9+/fZx1JK7lcjoiICDg5OeHKlSvcF1wAMHv2bISFhSE8PBzR0dFYsmQJ60haSSQSvP/++3jttdeQnp6Onj17so6kkVwuR0JCAhwcHHDr1i00NDSwjqRV7969sWzZMixatAhHjx5V7bQSPlGPLSPu7u4dCkOJRILMzEyGibTLzc3Fxx9/jMrKSpiZmSE6Ohp2dnasY2m0adMmmJiY4MqVK1ixYgVSU1M7rHzxJjExEV9//TUcHR3x888/429/+xuWL1/OOpZG69evf+w1nntsO+sZ571X/Ny5c9ixYweMjIwglUoRHh6O8ePHs46lUUlJCbKzszF//nxkZGRg9OjRGDhwIOtYT53a2loUFBTg9ddfx5UrVzBixAj069ePdSy1fvrpJ2RkZGDVqlX46quvMHr0aDg6OrKOpVFzczPKysowdOhQFBUVwcbGhvuFqGcZFbakS+rq6lBeXg5ra2v079+fdRythF454Z9eXl44duwY61hqLViwAEePHoW+vj7kcjm8vLxw4sQJ1rG0Etsq84ULF3Dz5k3Y2trC1dWVdRyd1dTUoF+/fujWrRvrKGpdvnxZ7Xu8ruRryqXp/w9LKSkpat/jcQfijz/+UPuera3tE0yiu5iYGLXnX9atW/eE0xBdUSvCE7Zt2zaEhoZizpw5jz3x8VxwAUB6ejr27Nmjemr19/fH7NmzWcfSqKWlBffv34dEIkFjY6PqQBavlEol9PXbvi0NDAxgYGDAOJF27VeZExISuF9ljomJQWlpKcaOHYvTp08jLy8PwcHBrGN1asGCBWp/sPL6eXH27Fm17/Fa2PJavGpSVVXFOkKXhIaGdvq6RCLhdirJkCFDWEcg/we0YvuEVVdXw8zMDM7Ozpg4cSJeeOEFvPbaa+jVqxf3I08WLFiAhIQE9O7dG1KpFEuXLuV+NfHKlSvYvHkzqqurYWFhgZCQEEyaNIl1LLU+/vhj3LlzB+PGjUNeXh6srKy4LboEYltlbr9qr1Qq4enpibS0NMapOvfnn3+qfY/XzwtNB2t43b5dt26d2geImJiYJ5xGN2JcARUbMe4+EFqxfeKE0VnXrl1DcXExMjMzsWnTJpiammLv3r2M02kmkUjQu3dvAICRkREMDQ0ZJ9Kud+/e+Oabb1QPFLyv2IaEhODChQsoLi7GnDlz8MYbb7COpJXYVplbWlqgUCigp6cHpVLJ9ag9oXgtLS3Ft99+C7lcDqBtRuy2bdtYRlPLzc3tsT9T4c+Z13MEwuQXMQkNDYVEIsGja1O8roCKsd1DjLsPhApbZgoLC5GdnY2rV68CAPeHsABg0KBB+Pjjj+Hk5ITc3FwMGjSIdSStkpKScOPGDTg5OWHatGkYN24cl4VMa2srWltbsW7dOuzatQuvvPIKFAoFlixZwuUPqfbGjRuHgIAA1SrzSy+9xDqSRjNmzMDChQsxZswY5OfnY8aMGawjaRUcHIzJkyfjp59+grm5ORobG1lHUuuHH35gHaHLGhoaMHny5E77Vp2dnRkk0o73mcCP4rV41SQsLAz6+vo03ktkqLBlxNvbGwMHDsTatWvx+uuvs46jk/DwcKSkpODHH3/EkCFDVDM2eRYREQGlUolr164hOjoa//73v5Gdnc061mNOnDiB/fv3o7q6Gm5ublAqlejWrRvGjRvHOppWfn5+yMvLE80qs6+vLyZOnIjbt29j3rx5GDZsGOtIWvXo0QMrV65ESUkJIiIisGjRItaR1BLOEXTWH8xrX7AwPk1MfasBAQGIjY3tdOWQxyIyLi4Ofn5+nbZ98NruERwcjJiYmA67ELzvPhAqbJm5evUq8vLycPnyZSQkJMDU1BQ7d+5kHUsj4Ru7tbUVLS0tj22B8ejo0aPIzs5GZWUlXnzxRfj5+bGO1ClPT094enri+PHjmDdvHus4XbJixQokJydzX9AK2o8nu3jxIgwMDGBhYQFvb2/07duXYTL1lEolqqqq0NjYiMbGRjx48IB1JLXee+89AHjs8+zhw4cs4uhEmEvafuybUqlEXFwcq0haCRf8PFrE1tTUsIij1ZQpUwA83vbB888R4UKiR3chhBsBCZ/4bjh8itXX1+PevXu4c+cOHj58iOeff551JK02bdqE8vJyTJw4EX/++Sc+/PBD1pG0+v7771FVVYW33noLXl5e3K6OC4eXSktLsXPnzg6/eCdcA5yVlYXLly9zuVrUnkwmg7m5OWbMmAErKyvcu3cPzc3NXB/S8/f3R0ZGBtzd3TF16lS89tprrCOptW/fPgBt/cHCL5lMhsDAQMbJ1Nu8eTPy8/NVX9fW1mL58uX46aefGKbSbN26dVAoFB1ey8nJwdy5cxkl0ky4YczZ2Vn1a+jQoYiPj2ecTL0VK1ZAJpN1eO3MmTOiuGr5WUYrtoy88847cHV1xapVq2Bvb886jk5KS0tx9OhRAICrq6soDlwcPnwYTU1NuHLlCrZu3YrS0lJkZWWxjvUYCwsLAOIcLyO2a4Bra2tVDwyTJk2Cr68vAgMD4e3tzTiZevn5+aoRalOnTmWcRrPa2lrs2rULa9euBdBWCERHRyMoKIhxMvXi4uLg7++PPXv2oL6+HsHBwfDx8eH62nArKyuEhIQgKioKQNsDxYkTJ7i9qj0rKwvGxsaqnxu5ubkICgricuauwM3NDe+99x7i4+OhVCqxbds2/P7776Lrb37WUGHLyMmTJ1lH6DKZTIampib07NkTDx8+FMUVmZmZmbh48SIKCgowYsQI+Pr6so7UKWEE2YwZM5CamoqSkhLY29tj/vz5jJNp9+gtY5WVlYyS6EYqlaK4uBh2dnYoLi5GQ0MD6urquD6QdfHiRfz973/n+mIGQUxMDAIDAxEXF4e7d++iqKgISUlJXN86Zmdnh9jYWPj7+8PAwACffPIJHBwcWMfSaP369di+fTs+/PBD3Lt3D7169cLJkyfRp08f1tE6tX//frz77rvo0aMHKisrceLECezevRtjxoxhHU2tpUuXQi6XY/Xq1aiuroaLiwuOHTvG/eSXZx3NsSU6O3PmDD799FPY29vj1q1b+Mc//oG33nqLdSyNwsLCMG3aNDg7O3M5DeFRa9euhZWVFV588UXk5eWhtrYWkZGRrGNpFBsbi6SkJMjlcjx8+BA2NjYax+Swlp+fjy1btqCyshKWlpYIDQ1Ffn4+zMzMMH36dNbxOjVr1izU1NTA2toaEokEEomE24NYQNtINX9/fzx8+BAJCQncj9kTFBYWYsOGDYiLi1PtovBu8+bNKC8vR0JCAusoWkmlUvj6+sLAwADx8fEwMjJiHUkn8fHx+PHHH3H48GHWUYgOqLAlXXL//n3VlbomJias42h19+5dREVF4datW7CxsUFISAjX/czC1b8CHx8ffPHFFwwTaTdv3jwkJSUhPDwcy5Ytw9atW0XxQ1Ygl8u5X4Hp7KIGXi9oEHqsZTIZPvroIyxZsgRDhw4FwG+LSvtcDQ0NkEgk6NWrFwA+JwwA/3ulrkKhQHx8PFxdXVVtbTxu7wsXStTX1yMoKAhBQUGqvxe8XighXKmrVCrx1VdfwcHBQZWZrtTlF7UiEK3EeCuPYNOmTfD09MS4ceNw7do1bNiwgcunbmFOorW1NfLz8+Ho6IjCwkLY2NiwDaaDfv36oXv37mhoaMDgwYPR1NTEOpJGycnJOHz4sGqyh76+Pr777jvWsTTS19dHdHQ06urqMH36dAwfPpzbwrb9ar2Liwtu3LiBGzduAOC3sBWKV6FFRQzajyYTJqnwPK6s/ZW6AwYMUM3n5vVCCaDjmYc1a9YwTEK6ggpbopUYDomp8/DhQ7z55psA2g4C8Lr6KcxJVCqVuHr1KgwMDCCXy0Vxu5uFhQWOHz+Onj17IiYmBlKplHUkjdLS0pCYmIh9+/bBzc0NR44cYR1Jq02bNmHZsmWIi4uDk5MTQkJCkJqayjpWp4Se60OHDqkOvInFhx9+iOTkZNYxdNJ+NJkYiPHAlYuLC+sI5P+ACluilbOzM3799VeYm5vDxMQE//znPyGXy0Ux8qSlpQW3bt3C0KFDUVxczO3MRDHe1iTYtm0bKioq4ObmhlOnTmHXrl2sI2lkYmICc3NzNDQ0wMXFBbGxsawjaSWTyTBhwgTs27cPQ4YMEcUDj5gOvAl69eqF8PBw2NraqvqCedzWby8+Ph4HDx5Ejx49VK/x2j4BANnZ2Th8+HCHMVq8rtiuXbsWEokEdXV1aGhoUJ0vMTMzw6lTp1jHI2pQYUu02rNnD65evYrW1lb0798f/fr1g7m5OYKCgrieQQgAGzduRFBQEGpqamBmZobt27ezjtQpMd7WBED1wDNgwADVA4+5uTnrWBoZGxsjIyNDdQCrtraWdSStunfvjkuXLkGhUOCXX35B9+7dWUfSqq6uDpMmTRLNgTcAquugeb3koDPp6em4dOkSevbsyTqKTiIiIrBhwwZRHM4T+phXr16NyMhIGBkZobGxkfprOUeHx4hWnp6eSE1NhUwmg5ubG86fPw/g8YNOPLl48SK3lzF0prq6GmZmZqI6JKTugaewsJDrBx6pVIqysjKYmZkhISEBkydP5n7L8e7du4iMjERRURHs7OwQFBTE9fgsQFwH3tq7cOECbt68CVtbW7i6urKOo5Wfnx/27t0riqkvAPDuu+/i4MGDrGN0ydy5c3HixAnV13PmzBHlyM5nBa3YEq2EbU9DQ0NYW1urXuf5g/TQoUOiKmzNzMwAtA23P3XqVIcDWI/OieVFdna22gcengUEBKimNoSEhDBOoxsLCwvuWzweJaYDb4KYmBiUlpZi7NixOH36NPLy8ri+kQ5om+oxa9YsDBs2DEDb5zLPh3pNTU0RGhoKBwcH1c8Q3ts9Jk6cCB8fH4waNQr5+fmYPXs260hEAypsiVYymQwlJSVQKBQdfs/z3e9KpRJyubzTnlqet3G3bNkCHx8fVaHLMzE+8AD/24rQvo+S13FD7acIPHjwAH379lV9zXMfJSCuA2+CnJwcVbvE0qVL4enpyTiRdu+++y7rCF0ifFZUV1czTqK7tWvX4ubNm7h58yY8PDwwYsQI1pGIBlTYEq0MDQ2xadOmx37f/rACb65fvw43NzcolUpVoSX8PjMzk3E69YyMjPD222+zjqETMT7wAG2r4u0nIfA8bqh98cpz609nxHjgraWlBQqFAnp6eh0+O3jm4OCAvXv3ori4GDY2NvDz82MdSSN/f3/RtHsIc2zbKywsRHp6OvXZcowKW6KV8MNUmK8quHbtGqtIWo0ZM0ZURYBQwBgbG2P//v144YUXVB+ovM7+FOMDD9DWpvLXX3/B1NSUdZQuEUOR1Z4YD7zNmDEDCxcuxJgxY5Cfn48ZM2awjqTVhg0bMH78eLi7u+PatWsICQnB/v37WcdSS0ztHu3n2BLxoMKWaJWbm4vi4mJ89tlnWLZsGYC2226OHj2Kr7/+mnG6p4Mw1N7Y2BilpaUoLS1VvcdrYSu2B5779+8jNDQUv/76K/r27YuqqipMmDABoaGhornaU0zCwsIQGRmJuro6JCQkYMuWLawjabVkyRJMnDgRt2/fxrx580Rxcr+urk7V1z5y5EicO3eOcSLNxNTuIeyetbS0oKCgQHWpS2VlJeNkRBMqbIlWffr0QVVVFZqbm1U320gkEgQFBTFOpp6welhQUIDRo0erXr927RqcnZ1ZxVKL1wNimojtgSc8PBxvvvlmh7m1aWlp2LZtG6KiohgmU09YyVcqlXjw4EGH1gReH3gEZ86cQUBAALf9y+1VVVVBKpUiODgYUVFRGD58OBQKBXx9fXH8+HHW8TSSyWSoqqrCc889h+rqaigUCtaRNBJju4e/vz/kcjkqKyvR2toKc3NzvPXWW6xjETWosCVaDRs2DMOGDcP8+fMxYMAAAEBFRQUsLS0ZJ1Ovvr4ex44dw+HDh1VFV2trK5KSkrgsugRiGrYutgee8vJyzJo1q8Nr8+fPx1dffcUokXbtr6d1cHDo8DXvha2FhQViY2NRUVGBV199FW+++Sa3h26uX7+OI0eO4I8//lA9FOvp6XH/ZwwAgYGB8PLygrGxMaRSKcLCwlhH0kiM7R5SqRRffPEFNm7cqDoUSfhFhS3R2blz59CjRw/U19fj5MmTmDRpEtavX886Vqf69OmD6upq0RRdAjENWxfbA4+BgUGnr/O8YiSs5J8/fx6TJ09WvZ6ens4qks7c3d0xc+ZM5OTkYNeuXThw4AAKCgpYx+qUq6srXF1dRTf/GmibLpCZmYna2lr079+fdRytfH19O7R7CGPKeCbcntfU1IQePXpALpczTkQ0ocKW6Ozs2bNITEzEO++8g7Nnz3J9pa7Yii6BlZUV94evHiWWB56HDx+ipKTksRFw7WcG8+b8+fP4+eef8fXXX+Pnn38G0NbukZmZyf1Kl5+fH+7evYuXXnoJq1at4rIFSBAXFwc/Pz98+eWXOHPmTIf3eJ4JCwCpqalwd3cXRVELoMNnw8WLF2FgYAALCwt4e3t3GGfHk2nTpmHv3r0YMWIEPD09qSefc1TYEp1JJBJUVVXBzMwMEokEDx48YB1JK7EUXYL2w9aFlUTef7CK5YGn/eSG9nh+kBg5ciTq6upgaGio6lWVSCSYOXMm42TajRkzBi0tLaioqEB5eTkGDx7M7SnzKVOmAAC8vLwYJ+m65uZmeHh4dJjLzPNnhkwmw8CBA+Hk5ITr16+joKAA/fv3R3BwMLfTHOzs7ODi4gKJRILXX38dgwcPZh2JaECFLdGZi4sLfHx8EBMTg/DwcEybNo11JK3EUnQJxDZsHRDPA4/YpjgAwMaNG3Ho0CGUl5eLZr6xYOXKlQDaDnBGRUVhx44dyM/PZ5yqc0Lvr729PeLj41FSUgJ7e3usWLGCcTLtPvjgA9YRuqS2thY7d+4EAEyaNAm+vr4IDAyEt7c342TqffLJJ3j55ZcBAMOHD2echmhDhS3RmZ2dnera1FGjRoliLqVYii5eD4jpQiwPPGKb4gAADQ0NCAgIQF5eHsrKyjq8x/OqHNA27is3Nxc2Njbw9PTEvn37WEfSKjg4GG+88QY8PDyQm5uL4OBgxMXFsY6l0bBhw3D58uUOo6h4bvuQSqUoLi6GnZ0diouL0dDQgLq6OjQ2NrKOppZEIsHq1as7rIrTBQ38osKW6Ezo5QL4vpa2PbEUXe1Puz+K95PZYnngEdsUBwA4ePAgbty4gbKyMixYsIB1nC6ZMGECgoKC0NjYiH79+qkKAp7JZDIsWrQIQNsqLu8zYQEgICAANjY2KCoqgqGhIfcHT0NDQxEUFITKykpYWloiNDQU6enpWLVqFetoas2dO5d1BNIFVNgSnYmtlwsQT9ElnH7vbO4u78TywCPGA4UNDQ1wcnJCZGQk13+2nTEyMsLMmTNhbGyM+vp6hIWF4dVXX2Udq1N//PEHAMDExATffPMNnJyckJ+fD2tra8bJdLNt2zasX78eH330Eddb+gDg6OiIkydPqr6Wy+UdPvN4NGvWLJw6dQoVFRVwcXGBvb0960hEAypsic7E1ssFiKfoEuM2uUBsDzxiOlD42WefYf369di+fTskEolqooNEIsHnn3/OOJ1me/bsQVJSEgYMGIB79+7B39+f28I2NDRU9fukpCQkJSUB4HsUXHsymQxNTU2QSCRcb+kDQHJyMg4fPqxqndDX18d3333HOpZGmzdvhrm5Of71r39h1KhRCA4OxsGDB1nHImpQYUu0On36NDw8PHD79u3HPuh57uUCxFN0iXGbXCC2Bx4xHSgUCm7h4Jvgxo0bLOJ0Sbdu3VQr4wMGDIChoSHjROo9+ucrJt7e3jhy5AhGjx6NN954A2PHjmUdSaO0tDQkJiZi3759cHNzw5EjR1hH0qqsrAwfffQRcnNzMWXKFBw4cIB1JKIBFbZEq927d8PDwwO//fYbzM3NWcfpErEUXWLcJhfrA49YDhQCwKVLlxAVFYW+ffsiIiIClpaW2LlzJ9LT03HhwgXW8TQyMjJCYmIixo8fj5ycHPTr1491JLUCAgIQGxvbaT877wc7e/TogZSUFBgbG0NfX5/7XmwTExOYm5ujoaEBLi4uHa645lVraytqa2shkUgglUpF0S/+LKPClmhlZ2eHuXPnorS0FHZ2dqrXJRIJ/P39GSZTT6xFl5i2ycX6wCOWA4UAEB0djdjYWPz555+IiYlBTU0NLC0t8eWXX7KOptXo0aNRUVGB3bt3Y8iQIVxfICAUV7wXsZ359NNPkZaWhv79+6OqqgqrV69Gamoq61hqGRsbIyMjAxKJBMeOHUNtbS3rSFoFBgZi4cKFqKqqwoIFC7BhwwbWkYgGVNgSrQ4ePIjKykqEhoZi8+bNrOPoRKxFl5i2ycX4wAOI50Ah0NaiYmtrC1tbW2zcuBGrV6+Gp6cn61gapaWl4fjx46qRTgCQk5ODlpYWxsm0u3DhApKTkzvcRsd7L3Pv3r1VDw3PPfcc91MRtm/fjrKyMrz//vtISEjAli1bWEfSytnZGefOnUNtbS1MTExE03v9rKLClmilp6cHCwsLUfUVibXoEtM2uRgfeADxHCgE0GHL09LSkvuiFgBmz56NCRMmID4+XjXCSU9PD6ampoyTabdnzx6sX78eZmZmrKNoJVxy0NraipUrV2LcuHHIz8/n/u90QEAAEhISAAAhISGM02i2ePFitUUs7w88zzIqbMlTSaxFl5i2ycX4wAOI50AhADQ1NaGkpAQKhQIKhQIlJSWqyQjCFbu86d69O6ytrREWFsY6Spf17duX61al9oT//u3/HkydOpVVHJ0JrQjtv/94/bu8detWAIBSqURISAgiIyMZJyK6kCiFT0lCCHNnzpxRrSY2Nzdzv/oiRp3NBua1mFm8eHGnr4th3JeYpKSkAAAyMjJgYWGBF154QbVSx/thLLHpbBVUDH+XlyxZIoqchFZsCeGKmLbJxUaMBwqFMVT5+flwdHRUvS6GizvERBixN2bMGABAdXU1yzhPpSlTpnSYxWxgYAC5XM71GDgiTlTYEsIRMW2Ti40YDxSK+eIOMWnfd19TUwOZTMYwzdPp22+/hVKpxNatW+Hl5QVHR0f89ttvSE5OZh1NrebmZtXvlUol5HK5qjCnhQd+UWFLCEfEMndXjMR4oFDMF3eI0datW3Hx4kWYm5tDqVSqRlKR/39CIVheXq7afXBwcMDt27dZxtLIzc2twyrz9OnTAbR9D2ZmZrKMRjSgwpYQDohxm1xsxHigUIwXd4jZ9evXkZGRQQP4/4uMjY2xe/duODo64pdffoGVlRXrSGr98MMPAB7/nisuLmYVieiACltCOCDGbXKxEesUB0BcF3eI2aBBgyCTybifBStmO3bswKlTp5CVlYUhQ4ZgzZo1rCOpVVRUhMrKSkRHR+N//ud/oFQqoVAoEBMTI4pLUp5VVNgSwgExbpOTJ0dMF3eI2d27dzF58mQMHjxYtXNCrQj/Wb169YK3tzfrGDqpr6/H2bNnUVNTo+ppl0gkWLRoEeNkRBMqbAnhgBi3ycmTI6aLO8QoLS0N8+fPx/PPP4/nn39e9TrdMPVsu3TpEiIiIjoc3iT8o8KWEA6IeZuc/PeJ6eIOMbKwsAAATJo0iXESwpPMzEyYm5sjJSUFvXr16vAezTfmFxW2hBDCOTs7O5w/fx4AMGrUKBo19B8mFLRvv/024ySEJ+Hh4cjOzu4wlYTwj24eI4QQzvn4+OCLL75gHYOQZ1JBQQFGjx7NOgbRERW2hBDCOU9PTzQ3N9PFHYQ8QQEBAYiNjcWECRPQrVu3Du9dvnyZUSqiDRWmCD+QAAAAr0lEQVS2hBDCuc6u0KX5xoQ8GYsWLUJSUhLrGERH1GNLCCGcoos7CGFPT08Pq1ev7rBjsm7dOsapiDpU2BJCCKfo4g5C2Js7dy7rCKQLqBWBEEI4tXz5cty/f7/Tizvo4gBCCHkcFbaEEMIphUKh9uIOKysrRqkIIYRfVNgSQgghhJCngh7rAIQQQgghhPwnUGFLCCGEEEKeClTYEkIIIYSQpwIVtoQQQggh5Knw/wAaBmuXpA2BDAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (12,8))\n",
    "sns.heatmap(match_df[match_df.region == 'euw1'].drop(['region'], axis = 1).corr(), vmin = 0, vmax = 1, annot = True, cmap = cmap, lw = .5, linecolor = 'white')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb308868110>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (12,8))\n",
    "sns.heatmap(match_df[match_df.region == 'eun1'].corr(), vmin = 0, vmax = 1, annot = True, cmap = cmap, lw = .5, linecolor = 'white')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>win</th>\n",
       "      <th>firstBlood</th>\n",
       "      <th>firstTower</th>\n",
       "      <th>firstInhibitor</th>\n",
       "      <th>firstBaron</th>\n",
       "      <th>firstDragon</th>\n",
       "      <th>firstRiftHerald</th>\n",
       "      <th>towerKills</th>\n",
       "      <th>inhibitorKills</th>\n",
       "      <th>baronKills</th>\n",
       "      <th>dragonKills</th>\n",
       "      <th>riftHeraldKills</th>\n",
       "      <th>region</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>na1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>na1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>na1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>na1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>na1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9221</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>eun1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9222</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>eun1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9223</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>eun1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9224</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>eun1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9225</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>eun1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9226 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      win  firstBlood  firstTower  firstInhibitor  firstBaron  firstDragon  \\\n",
       "0       0           0           0               0           0            0   \n",
       "1       1           1           1               0           0            1   \n",
       "2       0           0           0               0           0            0   \n",
       "3       1           1           1               1           0            1   \n",
       "4       1           1           1               1           0            1   \n",
       "...   ...         ...         ...             ...         ...          ...   \n",
       "9221    0           0           0               0           0            0   \n",
       "9222    0           0           1               0           0            0   \n",
       "9223    1           1           0               1           1            1   \n",
       "9224    1           1           1               1           1            1   \n",
       "9225    0           0           0               0           0            0   \n",
       "\n",
       "      firstRiftHerald  towerKills  inhibitorKills  baronKills  dragonKills  \\\n",
       "0                   0           0               0           0            0   \n",
       "1                   0           1               0           0            2   \n",
       "2                   0           0               0           0            0   \n",
       "3                   1           8               1           0            2   \n",
       "4                   0          10               2           0            1   \n",
       "...               ...         ...             ...         ...          ...   \n",
       "9221                0           0               0           0            0   \n",
       "9222                0           2               0           0            1   \n",
       "9223                1           7               1           1            3   \n",
       "9224                0           9               1           2            4   \n",
       "9225                1           4               0           0            1   \n",
       "\n",
       "      riftHeraldKills region  \n",
       "0                   0    na1  \n",
       "1                   0    na1  \n",
       "2                   0    na1  \n",
       "3                   1    na1  \n",
       "4                   0    na1  \n",
       "...               ...    ...  \n",
       "9221                0   eun1  \n",
       "9222                0   eun1  \n",
       "9223                1   eun1  \n",
       "9224                0   eun1  \n",
       "9225                1   eun1  \n",
       "\n",
       "[9226 rows x 13 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "match_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.99913326, -0.99244131, -0.7964714 , ..., -0.62371444,\n",
       "        -1.15938506, -0.94860297],\n",
       "       [ 1.00086749,  1.00761626, -0.7964714 , ..., -0.62371444,\n",
       "         0.33255811, -0.94860297],\n",
       "       [-0.99913326, -0.99244131, -0.7964714 , ..., -0.62371444,\n",
       "        -1.15938506, -0.94860297],\n",
       "       ...,\n",
       "       [ 1.00086749, -0.99244131,  1.25553786, ...,  1.08992148,\n",
       "         1.0785297 ,  0.40637628],\n",
       "       [ 1.00086749,  1.00761626,  1.25553786, ...,  2.8035574 ,\n",
       "         1.82450128, -0.94860297],\n",
       "       [-0.99913326, -0.99244131, -0.7964714 , ..., -0.62371444,\n",
       "        -0.41341347,  0.40637628]])"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler = StandardScaler()\n",
    "scaler.fit(match_df.drop(['win', 'region'], axis = 1))\n",
    "scaled_data = scaler.transform(match_df.drop(['win', 'region'], axis=1))\n",
    "scaled_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0,\n",
       " 0.40486080420731807,\n",
       " 0.5696060886737182,\n",
       " 0.6815999552968989,\n",
       " 0.779646201989236,\n",
       " 0.860177072287346,\n",
       " 0.9117611720883858,\n",
       " 0.9405907525646877]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exp_var_ratio = []\n",
    "for n in range(0,8):\n",
    "    pca = PCA(n_components = n)\n",
    "    pca.fit(scaled_data)\n",
    "    pca.transform(scaled_data)\n",
    "    exp_var_ratio.append(sum(pca.explained_variance_ratio_))\n",
    "\n",
    "exp_var_ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Ratio of Variance Explained')"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (12, 8))\n",
    "ax = fig.add_subplot(111)\n",
    "plt.plot(range(1,9), exp_var_ratio, marker = 'o', markerfacecolor = 'orange', markersize = 6)\n",
    "for i,j in zip(range(1,9),exp_var_ratio):\n",
    "    ax.annotate('{:.2f}'.format(j),xy=(i-.2,j+.03))\n",
    "plt.xlabel('Number of Componenets', size = 15)\n",
    "plt.ylabel('Ratio of Variance Explained', size = 15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb309c89a50>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pca = PCA(n_components = 6)\n",
    "pca.fit(scaled_data)\n",
    "data_pca = pca.transform(scaled_data)\n",
    "\n",
    "pca_corr = pd.DataFrame(pca.components_, columns = match_df.drop(['win', 'region'], axis = 1).columns)\n",
    "\n",
    "plt.figure(figsize = (12,8))\n",
    "sns.heatmap(pca_corr, cmap = cmap, vmin = -1, vmax = 1, annot = True, lw = .5, linecolor = 'white')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logistic Regression\n",
    "\n",
    "For this final step, I will be performing a Logistic Regression on the overall dataset, as well as the data from each individual region. Then, I will compare the regression coefficients to understand what the most important win conditions for a game of League of Legends are, and whether or not this is consistent across all the regions accounted for in my data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import classification_report, confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.83      0.89      0.86      1368\n",
      "           1       0.88      0.82      0.85      1400\n",
      "\n",
      "    accuracy                           0.85      2768\n",
      "   macro avg       0.86      0.85      0.85      2768\n",
      "weighted avg       0.86      0.85      0.85      2768\n",
      "\n",
      "[[1213  155]\n",
      " [ 249 1151]]\n",
      "\n",
      "firstBlood         0.341680\n",
      "firstTower         0.893198\n",
      "firstInhibitor     1.372865\n",
      "firstBaron         0.223970\n",
      "firstDragon        0.285656\n",
      "firstRiftHerald    0.186730\n",
      "towerKills         0.468161\n",
      "inhibitorKills     0.158593\n",
      "baronKills        -0.320805\n",
      "dragonKills        0.103578\n",
      "riftHeraldKills   -0.327020\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "X = match_df.drop(['win', 'region'], axis = 1)\n",
    "y = match_df.win\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = .3)\n",
    "\n",
    "log = LogisticRegression()\n",
    "log.fit(X_train, y_train)\n",
    "y_pred = log.predict(X_test)\n",
    "\n",
    "print(classification_report(y_test, y_pred))\n",
    "print(confusion_matrix(y_test, y_pred))\n",
    "print()\n",
    "\n",
    "log_coeff_tot = pd.Series(log.coef_[0], index = match_df.drop(['win', 'region'], axis = 1).columns)\n",
    "print(log_coeff_tot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.85      0.86      0.85       303\n",
      "           1       0.84      0.83      0.84       279\n",
      "\n",
      "    accuracy                           0.85       582\n",
      "   macro avg       0.85      0.84      0.85       582\n",
      "weighted avg       0.85      0.85      0.85       582\n",
      "\n",
      "[[260  43]\n",
      " [ 47 232]]\n",
      "\n",
      "firstBlood         0.407424\n",
      "firstTower         1.252272\n",
      "firstInhibitor     1.986462\n",
      "firstBaron         1.055010\n",
      "firstDragon        0.924895\n",
      "firstRiftHerald   -0.132288\n",
      "towerKills         0.451542\n",
      "inhibitorKills     0.651056\n",
      "baronKills        -1.038161\n",
      "dragonKills        0.201589\n",
      "riftHeraldKills   -0.212597\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "X = match_df[match_df.region == 'na1'].drop(['win', 'region'], axis = 1)\n",
    "y = match_df[match_df.region == 'na1'].win\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = .3)\n",
    "\n",
    "log = LogisticRegression()\n",
    "log.fit(X_train, y_train)\n",
    "y_pred = log.predict(X_test)\n",
    "\n",
    "print(classification_report(y_test, y_pred))\n",
    "print(confusion_matrix(y_test, y_pred))\n",
    "print()\n",
    "\n",
    "log_coeff_na = pd.Series(log.coef_[0], index = match_df.drop(['win', 'region'], axis = 1).columns)\n",
    "print(log_coeff_na)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.85      0.88      0.86       279\n",
      "           1       0.88      0.84      0.86       276\n",
      "\n",
      "    accuracy                           0.86       555\n",
      "   macro avg       0.86      0.86      0.86       555\n",
      "weighted avg       0.86      0.86      0.86       555\n",
      "\n",
      "[[246  33]\n",
      " [ 45 231]]\n",
      "\n",
      "firstBlood         0.482114\n",
      "firstTower         0.942659\n",
      "firstInhibitor     1.157092\n",
      "firstBaron        -0.037798\n",
      "firstDragon        0.245041\n",
      "firstRiftHerald    0.484386\n",
      "towerKills         0.450493\n",
      "inhibitorKills     0.312561\n",
      "baronKills        -0.095854\n",
      "dragonKills       -0.022236\n",
      "riftHeraldKills   -0.500573\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "X = match_df[match_df.region == 'br1'].drop(['win', 'region'], axis = 1)\n",
    "y = match_df[match_df.region == 'br1'].win\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = .3)\n",
    "\n",
    "log = LogisticRegression()\n",
    "log.fit(X_train, y_train)\n",
    "y_pred = log.predict(X_test)\n",
    "\n",
    "print(classification_report(y_test, y_pred))\n",
    "print(confusion_matrix(y_test, y_pred))\n",
    "print()\n",
    "\n",
    "log_coeff_br = pd.Series(log.coef_[0], index = match_df.drop(['win', 'region'], axis = 1).columns)\n",
    "print(log_coeff_br)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.77      0.86      0.81       292\n",
      "           1       0.85      0.76      0.80       307\n",
      "\n",
      "    accuracy                           0.81       599\n",
      "   macro avg       0.81      0.81      0.81       599\n",
      "weighted avg       0.81      0.81      0.81       599\n",
      "\n",
      "[[252  40]\n",
      " [ 75 232]]\n",
      "\n",
      "firstBlood         0.637184\n",
      "firstTower         1.058122\n",
      "firstInhibitor     0.682226\n",
      "firstBaron        -0.685710\n",
      "firstDragon        0.451320\n",
      "firstRiftHerald    0.164983\n",
      "towerKills         0.472004\n",
      "inhibitorKills     0.557367\n",
      "baronKills         0.255827\n",
      "dragonKills        0.211822\n",
      "riftHeraldKills   -0.228113\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "X = match_df[match_df.region == 'kr'].drop(['win', 'region'], axis = 1)\n",
    "y = match_df[match_df.region == 'kr'].win\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = .3)\n",
    "\n",
    "log = LogisticRegression()\n",
    "log.fit(X_train, y_train)\n",
    "y_pred = log.predict(X_test)\n",
    "\n",
    "print(classification_report(y_test, y_pred))\n",
    "print(confusion_matrix(y_test, y_pred))\n",
    "print()\n",
    "\n",
    "log_coeff_kr = pd.Series(log.coef_[0], index = match_df.drop(['win', 'region'], axis = 1).columns)\n",
    "print(log_coeff_kr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.83      0.90      0.87       252\n",
      "           1       0.89      0.82      0.85       250\n",
      "\n",
      "    accuracy                           0.86       502\n",
      "   macro avg       0.86      0.86      0.86       502\n",
      "weighted avg       0.86      0.86      0.86       502\n",
      "\n",
      "[[227  25]\n",
      " [ 45 205]]\n",
      "\n",
      "firstBlood         0.437823\n",
      "firstTower         0.890748\n",
      "firstInhibitor     1.902106\n",
      "firstBaron         1.148662\n",
      "firstDragon        0.059851\n",
      "firstRiftHerald    0.044306\n",
      "towerKills         0.513538\n",
      "inhibitorKills    -0.015044\n",
      "baronKills        -0.668050\n",
      "dragonKills       -0.018830\n",
      "riftHeraldKills   -0.301763\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "X = match_df[match_df.region == 'euw1'].drop(['win', 'region'], axis = 1)\n",
    "y = match_df[match_df.region == 'euw1'].win\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = .3)\n",
    "\n",
    "log = LogisticRegression()\n",
    "log.fit(X_train, y_train)\n",
    "y_pred = log.predict(X_test)\n",
    "\n",
    "print(classification_report(y_test, y_pred))\n",
    "print(confusion_matrix(y_test, y_pred))\n",
    "print()\n",
    "\n",
    "log_coeff_euw = pd.Series(log.coef_[0], index = match_df.drop(['win', 'region'], axis = 1).columns)\n",
    "print(log_coeff_euw)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.81      0.89      0.85       261\n",
      "           1       0.88      0.80      0.84       271\n",
      "\n",
      "    accuracy                           0.84       532\n",
      "   macro avg       0.84      0.84      0.84       532\n",
      "weighted avg       0.85      0.84      0.84       532\n",
      "\n",
      "[[232  29]\n",
      " [ 55 216]]\n",
      "\n",
      "firstBlood         0.412184\n",
      "firstTower         0.493069\n",
      "firstInhibitor     1.509817\n",
      "firstBaron        -0.160211\n",
      "firstDragon        0.268782\n",
      "firstRiftHerald   -0.653887\n",
      "towerKills         0.524594\n",
      "inhibitorKills    -0.064378\n",
      "baronKills         0.356230\n",
      "dragonKills       -0.118523\n",
      "riftHeraldKills    0.029707\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "X = match_df[match_df.region == 'eun1'].drop(['win', 'region'], axis = 1)\n",
    "y = match_df[match_df.region == 'eun1'].win\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = .3)\n",
    "\n",
    "log = LogisticRegression()\n",
    "log.fit(X_train, y_train)\n",
    "y_pred = log.predict(X_test)\n",
    "\n",
    "print(classification_report(y_test, y_pred))\n",
    "print(confusion_matrix(y_test, y_pred))\n",
    "print()\n",
    "\n",
    "log_coeff_eun = pd.Series(log.coef_[0], index = match_df.drop(['win', 'region'], axis = 1).columns)\n",
    "print(log_coeff_eun)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>overall</th>\n",
       "      <th>na</th>\n",
       "      <th>br</th>\n",
       "      <th>kr</th>\n",
       "      <th>euw</th>\n",
       "      <th>eun</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>firstBlood</th>\n",
       "      <td>0.341680</td>\n",
       "      <td>0.407424</td>\n",
       "      <td>0.482114</td>\n",
       "      <td>0.637184</td>\n",
       "      <td>0.437823</td>\n",
       "      <td>0.412184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>firstTower</th>\n",
       "      <td>0.893198</td>\n",
       "      <td>1.252272</td>\n",
       "      <td>0.942659</td>\n",
       "      <td>1.058122</td>\n",
       "      <td>0.890748</td>\n",
       "      <td>0.493069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>firstInhibitor</th>\n",
       "      <td>1.372865</td>\n",
       "      <td>1.986462</td>\n",
       "      <td>1.157092</td>\n",
       "      <td>0.682226</td>\n",
       "      <td>1.902106</td>\n",
       "      <td>1.509817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>firstBaron</th>\n",
       "      <td>0.223970</td>\n",
       "      <td>1.055010</td>\n",
       "      <td>-0.037798</td>\n",
       "      <td>-0.685710</td>\n",
       "      <td>1.148662</td>\n",
       "      <td>-0.160211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>firstDragon</th>\n",
       "      <td>0.285656</td>\n",
       "      <td>0.924895</td>\n",
       "      <td>0.245041</td>\n",
       "      <td>0.451320</td>\n",
       "      <td>0.059851</td>\n",
       "      <td>0.268782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>firstRiftHerald</th>\n",
       "      <td>0.186730</td>\n",
       "      <td>-0.132288</td>\n",
       "      <td>0.484386</td>\n",
       "      <td>0.164983</td>\n",
       "      <td>0.044306</td>\n",
       "      <td>-0.653887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>towerKills</th>\n",
       "      <td>0.468161</td>\n",
       "      <td>0.451542</td>\n",
       "      <td>0.450493</td>\n",
       "      <td>0.472004</td>\n",
       "      <td>0.513538</td>\n",
       "      <td>0.524594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>inhibitorKills</th>\n",
       "      <td>0.158593</td>\n",
       "      <td>0.651056</td>\n",
       "      <td>0.312561</td>\n",
       "      <td>0.557367</td>\n",
       "      <td>-0.015044</td>\n",
       "      <td>-0.064378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>baronKills</th>\n",
       "      <td>-0.320805</td>\n",
       "      <td>-1.038161</td>\n",
       "      <td>-0.095854</td>\n",
       "      <td>0.255827</td>\n",
       "      <td>-0.668050</td>\n",
       "      <td>0.356230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dragonKills</th>\n",
       "      <td>0.103578</td>\n",
       "      <td>0.201589</td>\n",
       "      <td>-0.022236</td>\n",
       "      <td>0.211822</td>\n",
       "      <td>-0.018830</td>\n",
       "      <td>-0.118523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>riftHeraldKills</th>\n",
       "      <td>-0.327020</td>\n",
       "      <td>-0.212597</td>\n",
       "      <td>-0.500573</td>\n",
       "      <td>-0.228113</td>\n",
       "      <td>-0.301763</td>\n",
       "      <td>0.029707</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  overall        na        br        kr       euw       eun\n",
       "firstBlood       0.341680  0.407424  0.482114  0.637184  0.437823  0.412184\n",
       "firstTower       0.893198  1.252272  0.942659  1.058122  0.890748  0.493069\n",
       "firstInhibitor   1.372865  1.986462  1.157092  0.682226  1.902106  1.509817\n",
       "firstBaron       0.223970  1.055010 -0.037798 -0.685710  1.148662 -0.160211\n",
       "firstDragon      0.285656  0.924895  0.245041  0.451320  0.059851  0.268782\n",
       "firstRiftHerald  0.186730 -0.132288  0.484386  0.164983  0.044306 -0.653887\n",
       "towerKills       0.468161  0.451542  0.450493  0.472004  0.513538  0.524594\n",
       "inhibitorKills   0.158593  0.651056  0.312561  0.557367 -0.015044 -0.064378\n",
       "baronKills      -0.320805 -1.038161 -0.095854  0.255827 -0.668050  0.356230\n",
       "dragonKills      0.103578  0.201589 -0.022236  0.211822 -0.018830 -0.118523\n",
       "riftHeraldKills -0.327020 -0.212597 -0.500573 -0.228113 -0.301763  0.029707"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "win_conditions_dict = dict()\n",
    "win_conditions_dict['overall'] = log_coeff_tot\n",
    "win_conditions_dict['na'] = log_coeff_na\n",
    "win_conditions_dict['br'] = log_coeff_br\n",
    "win_conditions_dict['kr'] = log_coeff_kr\n",
    "win_conditions_dict['euw'] = log_coeff_euw\n",
    "win_conditions_dict['eun'] = log_coeff_eun\n",
    "win_conditions = pd.DataFrame(win_conditions_dict)\n",
    "win_conditions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb30ba057d0>"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('fivethirtyeight')\n",
    "win_conditions.plot(kind = 'bar', figsize = (12, 8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
